Contextual configuration adjuster for graphics

ABSTRACT

An embodiment of a graphics apparatus may include a context engine to determine contextual information, a recommendation engine communicatively coupled to the context engine to determine a recommendation based on the contextual information, and a configuration engine communicatively coupled to the recommendation engine to adjust a configuration of a graphics operation based on the recommendation. Other embodiments are disclosed and claimed.

CROSS-REFERENCE WITH RELATED APPLICATIONS

This application claims benefit to U.S. Non-Provisional patentapplication Ser. No. 16/572,161 filed on Sep. 16, 2019, which claimspriority to U.S. patent application Ser. No. 15/483,623 filed on Apr.10, 2017 and is now granted U.S. Pat. No. 10,460,415.

TECHNICAL FIELD

Embodiments generally relate to data processing and to graphicsprocessing. More particularly, embodiments relate to a contextualconfiguration adjuster for graphics.

BACKGROUND OF THE DESCRIPTION

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data. Various settings,parameters, and configurations may be applied to operations on graphicsdata.

BRIEF DESCRIPTION OF THE DRAWINGS

The various advantages of the embodiments will become apparent to oneskilled in the art by reading the following specification and appendedclaims, and by referencing the following drawings, in which:

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIGS. 2A-2D illustrate a parallel processor components, according to anembodiment;

FIGS. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments;

FIGS. 4A-4F illustrate an exemplary architecture in which a plurality ofGPUs are communicatively coupled to a plurality of multi-coreprocessors;

FIG. 5 illustrates a graphics processing pipeline, according to anembodiment;

FIG. 6A is a block diagram of an example of an electronic processingsystem according to an embodiment;

FIG. 6B is a block diagram of an example of a contextual configurationadjuster apparatus according to an embodiment;

FIG. 6C is a block diagram of another example of a contextualconfiguration adjuster apparatus according to an embodiment;

FIG. 6D is a block diagram of another example of a contextualconfiguration adjuster apparatus according to an embodiment;

FIG. 6E is a block diagram of another example of an electronicprocessing system according to an embodiment;

FIG. 7 is a block diagram of another example of a contextualconfiguration adjuster apparatus according to an embodiment;

FIGS. 8A to 8E are flowcharts of an example of a method of configuringgraphics according to an embodiment;

FIG. 9A is a block diagram of another example of an electronicprocessing system according to an embodiment;

FIG. 9B is a block diagram of an example of a contextual configurationadjuster apparatus according to an embodiment;

FIG. 9C is a flowchart of another example of a method of configuringgraphics according to an embodiment;

FIG. 10A is a block diagram of an example of a contextual configurationadjuster apparatus according to an embodiment;

FIG. 10B is a flowchart of another example of a method of configuringgraphics according to an embodiment;

FIG. 10C is a flowchart of another example of a method of configuringgraphics according to an embodiment;

FIG. 11 is an illustration of an example of a head mounted display (HMD)system according to an embodiment;

FIG. 12 is a block diagram of an example of the functional componentsincluded in the HMD system of FIG. 11 according to an embodiment;

FIG. 13 is a block diagram of an example of a general processing clusterincluded in a parallel processing unit according to an embodiment;

FIG. 14 is a conceptual illustration of an example of a graphicsprocessing pipeline that may be implemented within a parallel processingunit, according to an embodiment;

FIG. 15 is a block diagram of an example of a streaming multi-processoraccording to an embodiment;

FIGS. 16-18 are block diagrams of an example of an overview of a dataprocessing system according to an embodiment;

FIG. 19 is a block diagram of an example of a graphics processing engineaccording to an embodiment;

FIGS. 20-22 are block diagrams of examples of execution units accordingto an embodiment;

FIG. 23 is a block diagram of an example of a graphics pipelineaccording to an embodiment;

FIGS. 24A-24B are block diagrams of examples of graphics pipelineprogramming according to an embodiment;

FIG. 25 is a block diagram of an example of a graphics softwarearchitecture according to an embodiment;

FIG. 26 is a block diagram of an example of an intellectual property(IP) core development system according to an embodiment; and

FIG. 27 is a block diagram of an example of a system on a chipintegrated circuit according to an embodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a more thorough understanding of the present invention. However,it will be apparent to one of skill in the art that the presentinvention may be practiced without one or more of these specificdetails. In other instances, well-known features have not been describedin order to avoid obscuring the present invention.

System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment at least a portion of the components ofthe computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment the front end 208 coupleswith a scheduler 210, which is configured to distribute commands orother work items to a processing cluster array 212. In one embodimentthe scheduler 210 ensures that the processing cluster array 212 isproperly configured and in a valid state before tasks are distributed tothe processing clusters of the processing cluster array 212. In oneembodiment the scheduler 210 is implemented via firmware logic executingon a microcontroller. The microcontroller implemented scheduler 210 isconfigurable to perform complex scheduling and work distributionoperations at coarse and fine granularity, enabling rapid preemption andcontext switching of threads executing on the processing array 212. Inone embodiment, the host software can prove workloads for scheduling onthe processing array 212 via one of multiple graphics processingdoorbells. The workloads can then be automatically distributed acrossthe processing array 212 by the scheduler 210 logic within the schedulermicrocontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation the number of partition units 220A-220N is configuredto be equal to the number of memory units, such that a first partitionunit 220A has a corresponding first memory unit 224A, a second partitionunit 220B has a corresponding memory unit 224B, and an Nth partitionunit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment the memory crossbar 216 has aconnection to the memory interface 218 to communicate with the I/O unit204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 202 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 225 for processing. In oneembodiment the frame buffer interface 225 interfaces with one of thememory units in parallel processor memory, such as the memory units224A-224N of FIG. 2 (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIND)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2 and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed via the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234processing can be performed over consecutive clock cycles. In oneembodiment multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 245 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 234 or the L1 cache or processing cluster 214.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 324. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 324. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 324 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can executed via a single SIMD instruction.For example and in one embodiment, eight SIMT threads that perform thesame or similar operations can be executed in parallel via a singleSIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 324to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440-443 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 444-445, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440-443. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 433 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects 430-431,respectively, and each GPU 410-413 is communicatively coupled to GPUmemory 420-423 over GPU memory interconnects 450-453, respectively. Thememory interconnects 430-431 and 450-453 may utilize the same ordifferent memory access technologies. By way of example, and notlimitation, the processor memories 401-402 and GPU memories 420-423 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In one embodiment, some portionof the memories may be volatile memory and another portion may benon-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 426may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, N is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, N (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasingtechniques are used to ensure that the data stored in graphics memories433-434, M is data which will be used most frequently by the graphicsprocessing engines 431-432, N and preferably not used by the cores460A-460D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 431-432, N) within the caches 462A-462D, 456of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 462 and caches462A-462D, 426.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 446 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engine 431-432, N. Itcontains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 3) may be configured to perform the functionsof one or more of a vertex processing unit 504, a tessellation controlprocessing unit 508, a tessellation evaluation processing unit 512, ageometry processing unit 516, and a fragment/pixel processing unit 524.The functions of data assembler 502, primitive assemblers 506, 514, 518,tessellation unit 510, rasterizer 522, and raster operations unit 526may also be performed by other processing engines within a processingcluster (e.g., processing cluster 214 of FIG. 3) and a correspondingpartition unit (e.g., partition unit 220A-220N of FIG. 2). The graphicsprocessing pipeline 500 may also be implemented using dedicatedprocessing units for one or more functions. In one embodiment, one ormore portions of the graphics processing pipeline 500 can be performedby parallel processing logic within a general purpose processor (e.g.,CPU). In one embodiment, one or more portions of the graphics processingpipeline 500 can access on-chip memory (e.g., parallel processor memory222 as in FIG. 2) via a memory interface 528, which may be an instanceof the memory interface 218 of FIG. 2.

In one embodiment the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2,and/or system memory 104 as in FIG. 1, to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Saving, Learning, and/or Predicting Configuration Settings Example

Turning now to FIG. 6A, an embodiment of an electronic processing system600 may include a graphics subsystem 601, persistent storage media 602communicatively coupled to the graphics subsystem 601, memory 603communicatively coupled to the graphics subsystem 601, and a display 604communicatively coupled to the graphics subsystem 601. The system 600may further include a contextual configuration adjuster 605communicatively coupled to the graphics subsystem 601 to adjust aconfiguration of the graphics subsystem based on contextual information.

In some embodiments, the contextual configuration adjuster 605 mayinclude a context engine to determine the contextual information (asdescribed herein in more detail below). Some embodiments may furtherinclude a recommendation engine communicatively coupled to the contextengine to determine a recommendation based on the contextual information(as described herein in more detail below). For example, the contextualconfiguration adjuster 605 may adjust the configuration of the graphicssubsystem 601 based at least in part on the recommendation from therecommendation engine. The system 600 may further include a sense engine(e.g. including a sensor hub) communicatively coupled to the contextualconfiguration adjuster 605 to sense contextual data (as described hereinin more detail below). In addition, or alternative to the sense engine,in some embodiments the contextual adjuster 605 may get inputs fromhardware metrics, hardware counters, internal signal lines, etc., todetermine contextual data. For example, the system 600 may also includean application processor, and the graphics subsystem 601 may include agraphics processor (e.g. including multiple processors/cores foreither/each). For example, the graphics subsystem 601 may include arender engine and the contextual configuration adjuster 605 may adjustvarious parameters of the render engine.

Some embodiments of the system 600, may include a profiler to determineprofile information for a graphics application (as described herein inmore detail below). For example, the system 600 may include a neuralnetwork trainer to train a neural network to develop a configurationdecision network for the graphics application based on the profileinformation (as described herein in more detail below). The contextualconfiguration adjuster 605 may be further configured to adjust theconfiguration of the graphics subsystem 601 based on the configurationdecision network. For example, the contextual decision network mayinclude a decision tree produced by training the neural network.

In accordance with various embodiments, some aspects may be more focusedon user contextual information (e.g. embodiments including therecommendation engine and/or sense engine), while some aspects may bemore focused on applications (e.g. embodiments including the profiler,neural network trainer and/or configuration decision network). Someembodiments may include the various aspects implemented throughout athree-dimensional (3D)/Virtual Reality (VR)/Augmented Reality (AR)system to adjust complex configuration scenarios in both a head-mounteddisplay (HMD) and a host side GPU.

Embodiments of each of the above graphics subsystem 601, persistentstorage media 602, memory 603, display 604, contextual configurationadjuster 605, context engine, recommendation engine, sense engine,profiler, neural network trainer, and other system components may beimplemented in hardware, software, or any suitable combination thereof.For example, hardware implementations may include configurable logicsuch as, for example, programmable logic arrays (PLAs), FPGAs, complexprogrammable logic devices (CPLDs), or in fixed-functionality logichardware using circuit technology such as, for example, ASIC,complementary metal oxide semiconductor (CMOS) or transistor-transistorlogic (TTL) technology, or any combination thereof. Alternatively, oradditionally, these components may be implemented in one or more modulesas a set of logic instructions stored in a machine- or computer-readablestorage medium such as random access memory (RAM), read only memory(ROM), programmable ROM (PROM), firmware, flash memory, etc., to beexecuted by a processor or computing device. For example, computerprogram code to carry out the operations of the components may bewritten in any combination of one or more operating systemapplicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

For example, the system 600 may include similar components and/orfeatures as system 100, further configured to adjust configurationsettings or parameters based on context or context-basedrecommendations. For example, the graphics subsystem 601 may includesimilar components and/or features as the parallel processor 200,further configured with a contextual configuration adjuster as describedherein. The system 600 may also be adapted to work with a stereo headmounted display (HMD) system such as, for example, the system describedin connection with FIGS. 11-15 below. For example, various components ofthe system 600 may be distributed among a host system and the HMD (e.g.portions of the contextual configuration adjuster implemented on theHMD, portions of the contextual configuration adjuster implemented on ahost side driver/application/operating system (OS), and/or portions ofthe contextual configuration adjuster implemented on both the HMD GPUand host side GPU, etc.).

Turning now to FIGS. 6B to 6D, an embodiment of a contextualconfiguration adjuster 620 (FIG. 6B) may include a memory 622 which isaddressed by context data and provides information to a predictor 623which outputs settings which correspond to the context data. Anotherembodiment of a contextual configuration adjuster 630 (FIG. 6C) mayinclude a memory buffer 632 which is addressed by contextual informationincluding, for example, time of day, location, user preferences, etc.The memory buffer 632 may provide information to a predictor 633 tooutput a prediction used to adjust VR system settings for an improved oroptimal power/performance tradeoff (e.g. by adjusting frame rate,resolution, etc.). For example, the predictor 633 may include a machinelearning (ML) system that trains based on previous settings adjustmentby the user. Another embodiment of a contextual configuration adjuster640 (FIG. 6D) may include a processor 642 which uses machine learning(ML) to process inputs including history data, sensor data, etc., andsettings in accordance with the ML processing of the inputs. Theprocessor 642 may also input confirmation information from the user toadjust the settings.

Turning now to FIG. 6E, an embodiment of an electronic processing system650 may include a processor 651, persistent storage media 652communicatively coupled to the processor 651, memory 653 communicativelycoupled to the processor 651, a display 654, and a graphics subsystem655 communicatively coupled to the processor 651 and the display 654.The system 650 may further include a context engine 656 communicativelycoupled to the processor 651 to determine contextual information, arecommendation engine 657 communicatively coupled to the context engine656 to determine a recommendation based on the contextual information,and a configuration engine 658 communicatively coupled to therecommendation engine 657 and the graphics subsystem 655 to adjust aconfiguration of a graphics operation in the graphics subsystem 655based on the recommendation. For example, the graphics subsystem 655 mayinclude a render engine and the configuration engine 658 may adjustvarious parameters of the render engine. The system 650 may furtherinclude a sense engine 659 communicatively coupled to the processor 651to sense contextual data. For example, the processor 651 may include anapplication processor and/or a graphics processor (e.g. includingmultiple processors/cores for either/each).

For example, the contextual information may include one or more of thefollowing types of information: user, content, location, biometric,time, temperature, power, environmental, schedule, habit, andconfiguration settings for a prior frame. For example, therecommendation may include one or more of the following types ofrecommendations: a power setting, a performance setting, a highperformance mode, a balanced performance mode, a low power mode, a gamemode, a movie mode, and a configuration setting for a subsequent frame.For example, the graphics operation may include one or more of aresolution, a frame rate, a color precision, and a sampling rate.

In some embodiments of the system 650, the context engine 656 mayinclude a profiler 660 to determine profile information for a graphicsapplication. For example, the recommendation engine 657 may include aneural network trainer 661 to train a neural network to develop aconfiguration decision network for the graphics application based on theprofile information. The configuration engine 658 may be furtherconfigured to adjust the configuration of the graphics operation basedon the configuration decision network.

Embodiments of each of the above processor 651, persistent storage media652, memory 653, display 654, graphics subsystem 655, context engine656, recommendation engine 657, configuration engine 658, profiler 660,neural network trainer 661, and other system components may beimplemented in hardware, software, or any suitable combination thereof.For example, hardware implementations may include configurable logicsuch as, for example, programmable logic arrays (PLAs), FPGAs, complexprogrammable logic devices (CPLDs), or in fixed-functionality logichardware using circuit technology such as, for example, ASIC,complementary metal oxide semiconductor (CMOS) or transistor-transistorlogic (TTL) technology, or any combination thereof. Alternatively, oradditionally, these components may be implemented in one or more modulesas a set of logic instructions stored in a machine- or computer-readablestorage medium such as random access memory (RAM), read only memory(ROM), programmable ROM (PROM), firmware, flash memory, etc., to beexecuted by a processor or computing device. For example, computerprogram code to carry out the operations of the components may bewritten in any combination of one or more operating systemapplicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

For example, the system 650 may include similar components and/orfeatures as system 100, further configured to adjust configurationparameters based on contextual recommendations. For example, thegraphics subsystem 655 may include similar components and/or features asthe parallel processor 200, further configured with a context engine,recommendation engine, and/or configuration engine as described herein.The system 650 may also be adapted to work with a stereo head mounteddisplay (HMD) system such as, for example, the system described inconnection with FIGS. 11-15 below. For example, various components ofthe system 650 may be distributed among a host system and the HMD (e.g.portions of the context engine 656 implemented on the HMD, portions ofthe recommendation engine 657 implemented on a host sidedriver/application/OS, and/or portions of the configuration engine 658implemented on both the HMD GPU and host side GPU, etc.).

Sense Engine Example

For example, a sense engine may include a sensor hub communicativelycoupled to two dimensional (2D) cameras, three dimensional (3D) cameras,depth cameras, gyroscopes, accelerometers, inertial measurement units(IMUs), location services, microphones, proximity sensors, thermometers,biometric sensors, etc., and/or a combination of multiple sources whichprovide information to the context engine. The sensor hub may bedistributed across multiple devices. The information from the sensor hubmay include or be combined with input data from the user's devices (e.g.touch data).

For example, the user's device may include one or more 2D, 3D, and/ordepth cameras. The user may also carry a smartphone (e.g. in the user'spocket) and/or may wear a wearable device (e.g. such as a smart watch,an activity monitor, and/or a fitness tracker). The user's devices mayalso include a microphone which may be utilized to detect if the user isspeaking, on the phone, speaking to another nearby person, etc. Thesensor hub may include some or all of the user's various devices whichare capable of capturing information related to the user's actions oractivity (e.g. including an input/output (I/O) interface of the userdevices which can capture keyboard/mouse/touch activity). The sensor hubmay be directly coupled to the capture devices of the user's devices(e.g. wired or wirelessly) or the sensor hub may be able to integrateinformation from the devices from a server or a service (e.g.information may be uploaded from a fitness tracker to a cloud service,which the sensor hub may download).

Context Engine Example

The context engine may collect information from a variety of sources todetermine the content currently being viewed. For example, the contentmay be determined to be a video game or a movie. Even in the context ofa video game, some types of games may benefit more from higherperformance graphics (e.g. action genre) versus others (e.g. puzzlegames). Even in the context of a movie, some types of movies may benefitmore from higher performance graphics (e.g. action vs comedy). Even inthe context of a particular genre video game or movie, some scenes maybe more demanding than others (e.g. an action sequence in the gameversus a stage between levels, an action sequence in a comedy movie,etc.). For example, a graphics application may be able to directlyprovide information about the genre of the game and/or the demands of ascene to the context engine. In addition, or alternatively, imageprocessing or machine vision processing may be performed to identify thecontext of the game/movie/scene. The context engine may also collectdata from hardware metrics associated with previous rendered frames.

The context engine may also collect information from a variety ofnumerous sources to determine the environment of the user, the activityof the user, the emotional state of the user, and other contextualinformation. Some contextual information may be derived from the sensorinformation. The context engine may collect, for example,schedule-related information which may include calendar information,reminder information, and/or alarm information (e.g. from correspondingapplications on the user's computer, apps on the user's smartphone orwearable devices, and/or the user's cloud services), location-relatedinformation for the user, and/or habit-related information for the user.The context engine may also maintain its own schedule information forthe user to integrate the user's schedule-related information.Alternatively, or in addition, the context engine may link to otherapplications or services (e.g. a calendar application or a calendarcloud service) that contain the user's schedule-related information.

Some embodiments of the context engine may leverage the integration ofmultiple different technologies. One technology may include utilizingmachine vision to monitor and/or analyze a user's activity. Anothertechnology may utilize machine learning elements (e.g. of a wearable forpersonal movement, ergonomic characterization, and/or to learn a user'shabits). Another technology may include schedule integration tounderstand scheduled events, such as meetings, appointments, reminders,alarms, and/or breaks between scheduled events. Another technology mayinclude location integration, including locations within buildings.Another technology may include applying intelligence (e.g. contextualintelligence, artificial intelligence, machine learning, etc.) acrossthe technologies (e.g. movement, day planning, location, habits, etc.)to make configuration recommendations to improve the user's experiencewith their graphics devices and applications. For example, thecontextual information may include contextual information related to atleast one other person (e.g. a nearby person, notifications from anotheruser, etc.).

Some embodiments of a machine vision system, for example, may analyzeand/or perform feature/object recognition on images captured by acamera. For example, the machine vision system may be configured toperform facial recognition, gaze tracking, facial expressionrecognition, and/or gesture recognition including body-level gestures,arm/leg-level gestures, hand-level gestures, and/or finger-levelgestures. The machine vision system may be configured to classify anaction of the user. In some embodiments, a suitably configured machinevision system may be able to determine if the user is present at acomputer, typing at a keyboard, using the mouse, using the trackpad,using the touchscreen, using a HMD, using a VR system, sitting,standing, and/or otherwise taking some other action or activity.

Recommendation Engine Example

At a high level, some embodiments of a recommendation engine may use thecontextual information to set a high level mode for the graphics system.For example, a most demanding content/user context may get arecommendation of a performance mode. A least demanding content/usercontext may get a recommendation of a power saving mode. An in betweencontent/user context may get a recommendation of a balanced mode. At alow level (e.g. a detailed configuration level), such recommendationsmay further include specific parameters customized for the user (e.g. auser who is less sensitive to red colors may have parameters passed tothe GPU where the red portion of a color mask is processed with 16-bitprecision instead of 24-bit precision). Another example is that later inthe day or anytime the user appears to be less alert or engaged, allcolors may be processed with 16-bit precision instead of 24-bitprecision.

The recommendation engine may use schedule-related context informationto make a configuration recommendation. For example, the recommendationengine may recommend that the graphics system enter a deep sleep stateduring a scheduled event where the user is unlikely to use the graphicssystem (e.g. based on a further context such as the location being theuser's work location). The recommendation engine may recommend that thegraphics system move from the deep sleep state to a standby power statenear or at the end of the scheduled event (e.g. if there is a break inthe user's schedule and/or also based on further context informationsuch as the user standing or walking which may indicate that thescheduled event has concluded).

The recommendation engine may be also able to use the determinations ofthe machine vision system to make better configuration recommendations.The machine vision system may also monitor the user's action or responsefollowing a configuration change to determine what the user did inresponse to the configuration change. If the user didn't like the newconfiguration (e.g. as indicated by the user changing the settings backor to some other settings), the recommendation engine may make adifferent recommendation in a similar situation in the future. Devicesfrom the sensor hub may additionally or alternatively be used to monitorthe user.

The machine learning system may learn the patterns of the user and therecommendation engine may make configuration recommendations based onthe user's habits. Some embodiments of the machine learning system, forexample, may receive information from various sources to learn theuser's habits, preferences, and other information which may be useful inmaking better configuration recommendations. For example, the user mayplay a game or watch a movie at about the same time every day. Themachine learning system may receive or monitor information related tothe activity (e.g. time, duration, engagement level, etc.) and may learnfrom that information that the activity appears to be a habit of theuser. Advantageously, some embodiments of the recommendation engine mayinclude pro-active or predictive configuration aspect. For example, therecommendation engine may recommend that the graphics system pre-loadgame assets and/or activate various shaders based on a habit of the user(e.g. predicting that the user will be playing the game soon).

Some embodiments of the machine learning system may also learnlocation-related information. For example, the machine learning systemmay integrate a map of the user's residence or workplace. Even without amap or location service (e.g. a global satellite position (GPS)service), the machine learning system may keep track of locations tolearn useful information for the recommendation engine to make betterconfiguration recommendations.

Configuration Engine Example

Embodiments of a configuration may receive the contextual informationdirectly and make adjustments based on that information (e.g. for lowlevel settings) and/or may receive recommendations and map therecommendations to adjustments of specific parameters or settings. Forexample, if the recommendation is for a performance mode theconfiguration engine may adjust various controllable parameters toincrease or maximize the graphics performance (e.g. set a highestpossible resolution, set a highest possible frame rate, etc.). In someembodiments, the configuration engine may make further decisions basedon other information available to the configuration engine to defer orignore the recommendation (e.g. a request to pre-load game assets may beignored if insufficient resources are available). In some embodiments,portions of the configuration engine may be integrated with or tightlycoupled to the render engine to adjust various render parameters.

Turning now to FIG. 7, an embodiment of a graphics apparatus 700 mayinclude a context engine 721 to determine contextual information, arecommendation engine 722 communicatively coupled to the context engineto determine a recommendation based on the contextual information, and aconfiguration engine 723 communicatively coupled to the recommendationengine 722 to adjust a configuration of a graphics operation based onthe recommendation. For example, the configuration engine 723 may adjustvarious parameters of a render engine.

For example, the contextual information may include one or more of user,content, location, biometric, time, temperature, power, environmental,schedule, habit, and configuration settings for a prior frame. Forexample, the recommendation may include one or more of a power setting,a performance setting, a high performance mode, a balanced performancemode, a low power mode, a game mode, a movie mode, and a configurationsetting for a subsequent frame. For example, the graphics operation mayinclude one or more of a resolution, a frame rate, a color precision,and a sampling rate.

In some embodiments, the context engine 721 may include a sensor hub 724to sense contextual data, and the context engine 721 may determine thecontextual information based on the sensed contextual data. For example,the sensed contextual data may include one or more of a brightness ofthe environment, a temperature of the environment, machine visioninformation of the environment, movement information of the user,biometric information of the user, gesture information of the user,facial information of the user, and machine vision information of theuser.

In some embodiments, the recommendation engine 722 may include arecommendation store 725 to store one or more recommendations inassociation with saved contextual information. For example, therecommendation engine 722 may be configured to compare the contextualinformation from the context engine 721 with the saved contextualinformation, and retrieve a stored recommendation associated with savedcontextual information based on the comparison. Some embodiments of therecommendation engine 722 may further include a configuration decisionnetwork 726 to input the contextual information and output a recommendedconfiguration, where the recommendation engine 722 may then determinethe recommendation based on the recommended configuration from theconfiguration decision network 726. In some contexts, the recommendationmay be based on a predicted user preference for the configuration of thegraphics operation.

Some embodiments of the apparatus 700 may include a profiler 727 todetermine profile information for a graphics application. For example,the recommendation engine may include a neural network trainer 728 totrain a neural network to develop a configuration decision network forthe graphics application based on the profile information and theconfiguration engine 723 may be configured to adjust the configurationof the graphics operation based on the configuration decision network.

Embodiments of each of the above context engine 721, recommendationengine 722, configuration engine 723, sensor hub 724, recommendationstore 725, configuration decision network 726, profiler 727, neuralnetwork trainer 728, and other components of the apparatus 700 may beimplemented in hardware, software, or any combination thereof. Forexample, portions or all of the apparatus 700 may be implemented as partof the parallel processor 200, further configured with one or moreaspects of embodiments as described herein in connection with FIGS. 6Ato 10C. The apparatus 700 may also be adapted to work with a stereo headmounted system such as, for example, the system described in connectionwith FIGS. 11-15 below. For example, hardware implementations mayinclude configurable logic such as, for example, PLAs, FPGAs, CPLDs, orin fixed-functionality logic hardware using circuit technology such as,for example, ASIC, CMOS, or TTL technology, or any combination thereof.Alternatively, or additionally, these components may be implemented inone or more modules as a set of logic instructions stored in a machine-or computer-readable storage medium such as RAM, ROM, PROM, firmware,flash memory, etc., to be executed by a processor or computing device.For example, computer program code to carry out the operations of thecomponents may be written in any combination of one or more operatingsystem applicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

Turning now to FIGS. 8A to 8E, an embodiment of a method 800 ofconfiguring graphics may include determining contextual information atblock 831, determining a recommendation based on the contextualinformation at block 832, and adjusting a configuration of a graphicsoperation based on the recommendation at block 833. For example, themethod 800 may also include predicting a user preference for theconfiguration of a graphics operation at block 834.

At block 835, for example, the contextual information may include one ormore of: user, content, location, biometric, time, temperature, power,environmental, schedule, habit, and configuration settings for a priorframe. At block 836, for example, the recommendation may include one ormore of a power setting, a performance setting, a high performance mode,a balanced performance mode, a low power mode, a game mode, a moviemode, and a configuration setting for a subsequent frame. At block 837,for example, the graphics operation may include one or more of aresolution, a frame rate, a color precision, and a sampling rate.

Some embodiments of the method 800 may further include sensingcontextual data at block 840, and determining the contextual informationbased on the sensed contextual data at block 841. At block 842, forexample, the sensed contextual data may include one or more of abrightness of the environment, a temperature of the environment, machinevision information of the environment, movement information of the user,biometric information of the user, gesture information of the user,facial information of the user, and machine vision information of theuser.

Some embodiments of the method 800 may further include storing one ormore recommendations in association with saved contextual information atblock 850, comparing the contextual information from the context enginewith the saved contextual information at block 851, and retrieving astored recommendation associated with saved contextual information basedon the comparison at block 852.

In some embodiments, the method 800 may further include loading aconfiguration decision network at block 860, inputting the contextualinformation into the configuration decision network at block 861,outputting a recommended configuration from the configuration decisionnetwork at block 862, and determining the recommendation based on therecommended configuration from the configuration decision network atblock 863.

Some embodiments of the method 800 may further include determiningprofile information for a graphics application at block 870, training aneural network to develop a configuration decision network for thegraphics application based on the profile information at block 871, andadjusting the configuration of the graphics operation based on theconfiguration decision network at block 872.

Embodiments of the method 800 may be implemented in a system, apparatus,GPU, PPU, or a graphics processor pipeline apparatus such as, forexample, those described herein. More particularly, hardwareimplementations of the method 800 may include configurable logic suchas, for example, PLAs, FPGAs, CPLDs, or in fixed-functionality logichardware using circuit technology such as, for example, ASIC, CMOS, orTTL technology, or any combination thereof. Alternatively, oradditionally, the method 800 may be implemented in one or more modulesas a set of logic instructions stored in a machine- or computer-readablestorage medium such as RAM, ROM, PROM, firmware, flash memory, etc., tobe executed by a processor or computing device. For example, computerprogram code to carry out the operations of the components may bewritten in any combination of one or more operating systemapplicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. For example, the method 800 may be implemented on a computerreadable medium as described in connection with Examples 32 to 43 below.

For example, embodiments or portions of the method 800 may beimplemented in applications (e.g. through an API) or driver software.Other embodiments or portions of the method 800 may be implemented inspecialized code (e.g. shaders) to be executed on a GPU. Otherembodiments or portions of the method 800 may be implemented in fixedfunction logic or specialized hardware (e.g. in the GPU).

Advantageously, some embodiments may provide a predictive system toimprove or optimize VR system performance and battery life. For example,some embodiments may use time of day, location, history and/or userpreferences to predict optimal settings of VR system for optimizedbattery life. For example, the system may identify the context of whatgame the user is playing (e.g. a genre of the game, action game vs.puzzle game) and whether the user is outdoors or indoors (e.g. for abrightness setting, brighter for outdoors), what time of day, etc. toadjust the settings of the graphics system. If the user is in a noisyenvironment, the system may increase the audio volume or use noisecancellation automatically.

Advantageously, settings may not be not hard-wired or simplistic, butrather may be tuned to a user and a context (e.g. including the user'shabits). For example, an action game may not always get a recommendationfor high performance (e.g. which may use too much power). Instead, someembodiments may learn from what a user does and based on thatinformation, apply settings that are predicted to be aligned with theuser preferences. For example, the user may habitually adjust thesettings to a power saving mode when the battery life is less than 40%.The system may learn this and automatically make that adjustment for theuser, or if the user switched from a puzzle game to an action game withless than 40% battery life left, the system may recommend a balancedpower/performance setting instead of a performance setting. In anotherexample, when a user plays or uses a particular app, they may generallycontinue for a certain amount of time (e.g. 5 minutes, or 30 minutes,etc.). If there is sufficient battery life for at least double thetypical time, the system may recommend performance settings. If thebattery life supports only 1.5 times the typical time, the system mayrecommend balanced settings. If the battery life supports less than 1.1times the typical time, the system may recommend power saving settings.

The user preferences under the current context may be predicted andautomatically set so the user doesn't have to do anything to adjust thesettings. The settings then may be remembered so the same settings canbe applied under the same or a similar context in the future. Likewise,if the user changes the settings which were automatically applied, thesystem may learn these preferred settings and store them in associationwith the context to make a better configuration recommendation in thefuture. For example, the settings may represent a preferredpower/performance tradeoff that the user wants in that context.

Some embodiments may include a software layer that looks at all theparameters, the user's history, and makes a decision on the resolution,the frame rate, etc. Some embodiments may be implemented in an HMD, butother systems/devices may benefit from the advantages of someembodiments. For example, some embodiments may be implemented in othergraphics devices/systems including a smartphone, tablet, laptop,console, Smart TV, etc.

Advantageously, some embodiments may provide a context aware HMD basedon settings generated using context data. For example, the HMD mayremember settings based on user preferences, context, location, content(e.g., game v. movie, engaged v. not engaged), and use that informationto improve performance and/or the user experience (e.g., lower powerresolution if not needed, change what is rendered if near sighted,etc.). Some embodiments may improve or optimize power usage. Forexample, power optimizations may be based on content, engagement andother metrics. For example, some embodiments may adjust settings (e.g.modulate high resolution vs. low resolution, high polygon vs. lowpolygon based models) based on content and context. Some embodiments mayutilize active user sensing and other inputs to train the rendering.

Some embodiments may also provide a context aware HMD based on settingsfrom prediction, learning, and user confirmation. For example, someembodiments may use history data and sensor data to perform machinelearning, which can be modified by user input, to provide settings.Machine learning of other users input can be used to automaticallypredict the initial best parameters (e.g. provided with the applicationor downloaded from a cloud service). This may be useful, for example,for off the shelf content usages and may improve the user experience bynot having the user fiddle with settings to get their optimal VRexperience for different kinds of content.

For example, some embodiments may take into account the content that isbeing presented, the user who is viewing the content, the time, mentalcapacity or alertness (e.g. more alert in morning, less so at end ofday; the user may not perceive as much detail or differences), and othercontextual information to adjust the settings for a HMD. According toone aspect of some embodiments, the user may initially indicate to gowith default settings and then the HMD may adjust those settings basedon perceiving how the user is responding to the content itself.According to another aspect of some embodiments, preference settings maybe applied on a per-use-case basis. For example, the HMD performancesettings could be different for a video game versus watching a movieversus collaborative editing. The HMD settings may depend on contextincluding, for example, content that is being viewed, location (at homeversus office), who the user is viewing the content with, what is aroundthe user, etc.

At different times and under different contexts, the user may notperceive as much difference in terms of graphics quality. For example, a360 video is different than a fast moving game, so the HMD and/or hostGPU settings may be adjusted differently in accordance with someembodiments. For a fast moving game, the user may care most about aparticular object or character on the screen at a particular time, therest may be blurred or processed with less detail/resolution. For avideo, the whole screen may be important to the user because there maybe no preferred focus area in the scene. Some embodiments mayadvantageously use those contextual differences to improve/decreaseperformance (e.g. select better power/performance tradeoffs).

In some embodiments, the HMD may learn preferred user settings by havingthe user go through a setup process (e.g. to identify preferences forvarious types of content). For example, selecting a high performancesetting may indicate that the user wants that game/content processed athigh graphics performance without regard to power consumption.Additionally, or alternatively, some embodiment may use machine learningto predict and/or remember various settings based on userresponse/habits or based on demographic information (e.g. male, 20-30years old, etc.; the system may have information in a database and takeaverage of other user preferred settings). Some embodiments may alsomake additional adjustments based on information about the user's vision(e.g. if the user is wearing glasses or contacts).

Once the settings are customized for user/context, the system may applyother graphics processing such as like foveated resolution rendering(e.g. high resolution where the user is looking, lower resolutionelsewhere). The customized settings may adjust various performancefrequencies (e.g. frame rate, shading rate, etc.) based on alertness (90fps in morning, 60 fps in evening). The customized settings may alsoadjust encoding preferences (60 fps content rendered at 60 fps inmorning, but 30 fps interpolated in the evening).

The customized settings may also extend the battery life based on thecontext. In some contexts, the user may want to preserve battery life atthe expense of graphics performance. For example, if the user iswatching a movie with 15 minutes left, the system may determine thatbattery will be drained in 10 min at current settings but 20 minutesunder power saving mode. The system may adjust other settings inaddition to or alternative to graphics settings. For example, the systemmay determine that reducing radio transmission power may preserve enoughbattery life without decreasing the graphics quality (e.g. reducingpower to or temporarily turning off BLUETOOTH if it is not currentlyneeded to stream the video content).

Some embodiments may be directed to remembering user settings and savingcontexts while other embodiments may be directed to using machinelearning to predict what the user is going to need in terms ofpower/performance tradeoffs and then correcting it based on the user'sresponse to the changes and/or user input. The prediction may becompared with user feedback. For example, the confirmation from the usermay be active confirmation (e.g. the user changes settings; the HMDprompts the user to indicate if they are comfortable, etc.) or theconfirmation may be passive confirmation (e.g. the user keeps settings)or sensed confirmation (e.g. sensors to measure indications of fatiguesuch tears, blinking, eye-rolls, head movement, etc.). On the HMD side,for example, more intelligence may be built in to store user settingsand/or utilize sensors in the HMD to measure engagement with thecontent. If resolution/frame rate gets reduced (e.g. an automaticadjustment based on the context) and that causes the user to becomedisengaged, then the resolution/frame rate can be incrementallyincreased until the user appears to be engaged.

Application Level Machine Learning Configuration Examples

Turning now to FIG. 9A, an embodiment of an electronic processing system900 may include a processor 901, persistent storage media 902communicatively coupled to the processor 901, memory 903 communicativelycoupled to the processor 901, a display 904, and a graphics subsystem905 communicatively coupled to the processor 901 and the display 904.The system 900 may further include a profiler 906 to determine profileinformation for a graphics application, and a configuration developer907 communicatively coupled to the profiler 906 to develop aconfiguration decision network for the graphics application based on theprofile information. For example, the configuration developer 907 mayinclude a neural network trainer 908 to train a neural network todevelop the configuration decision network for the graphics applicationbased on the profile information.

For example, the processor 901 may include an application processorand/or a graphics processor (e.g. including multiple processors/coresfor either/each). In some embodiments of the system 900, the graphicssubsystem 905 may be configured to run the graphics application and thesystem 900 may further include a configuration engine 909communicatively coupled to the graphics subsystem 905 to adjust aconfiguration of a graphics operation based on the configurationdecision network. For example, the graphics subsystem 905 may include arender engine and the configuration engine 909 may adjust variousparameters of the render engine.

For example, the profile information may include one or more of:application execution time, wake time of a GPU unit, application states,active shaders, constant values, execution time of a unit of work (e.g.a draw or render pass), cache hit/miss rate, register pressure, memoryusage, performance bottleneck, occupancy/busy-ness of a hardware block,and other hardware and software metrics.

For example, the configuration decision network may include a decisionpath for one or more of: sleep time for a GPU unit, wake time for a GPUunit, GPU frequency, ring frequency, CPU frequency, memory frequency,resolution, frame rate, sampling rate, cache configuration, dispatchwidth, resource cacheability settings, compiler optimization settings(e.g. loop unrolling preferences, etc.), thread group walk order forcompute shaders, shading frequency, enabling or disabling hardwarefeatures, and power configuration/state (e.g. for various parts of theGPU such as slices, subslices, compute engines, etc.).

Embodiments of each of the above processor 901, persistent storage media902, memory 903, display 904, graphics subsystem 905, profiler 906,configuration developer 907, neural network trainer 908, configurationengine 909, and other system components may be implemented in hardware,software, or any suitable combination thereof. For example, hardwareimplementations may include configurable logic such as, for example,PLAs, FPGAs, CPLDs, or in fixed-functionality logic hardware usingcircuit technology such as, for example, ASIC, CMOS or TTL technology,or any combination thereof. Alternatively, or additionally, thesecomponents may be implemented in one or more modules as a set of logicinstructions stored in a machine- or computer-readable storage mediumsuch as RAM, ROM, PROM, firmware, flash memory, etc., to be executed bya processor or computing device. For example, computer program code tocarry out the operations of the components may be written in anycombination of one or more operating system applicable/appropriateprogramming languages, including an object-oriented programming languagesuch as PYTHON, PERL, JAVA, SMALLTALK, C++, C# or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages.

For example, the system 900 may include similar components and/orfeatures as system 100, further configured to adjust configurationparameters of a GPU based on neural network recommendations. Forexample, the graphics subsystem 905 may include similar componentsand/or features as the parallel processor 200, further configured with aconfiguration engine as described herein. The system 900 may also beadapted to work with a stereo head mounted display (HMD) system such as,for example, the system described in connection with FIGS. 11-15 below.For example, various components of the system 900 may be implemented ina host system GPU and/or the HMD GPU (e.g. with respective configurationdecision networks specific to each device and their respectiveworkloads).

Turning now to FIG. 9B, an embodiment of a graphics apparatus 920 mayinclude a profiler 926 to determine profile information for a graphicsapplication, and a configuration developer 927 communicatively coupledto the profiler 926 to develop a configuration decision network for thegraphics application based on the profile information. For example, theconfiguration developer 927 may include a neural network trainer 928 totrain a neural network to develop the configuration decision network forthe graphics application based on the profile information. Someembodiments of the apparatus 920 may further include a graphicssubsystem 925 communicatively coupled to the profiler 926 to run thegraphics application and a configuration engine 929 communicativelycoupled to the graphics subsystem 925 to adjust a configuration of agraphics operation based on the configuration decision network. Forexample, the graphics subsystem 925 may include a render engine and theconfiguration engine 929 may adjust various parameters of the renderengine.

For example, the profile information may include one or more of:application execution time, wake time of a GPU unit, application states,active shaders, constant values, execution time of a unit of work (e.g.a draw or render pass), cache hit/miss rate, register pressure, memoryusage, performance bottleneck, occupancy/busy-ness of a hardware block,and other hardware and software metrics.

For example, the configuration decision network may include a decisionpath for one or more of: sleep time for a GPU unit, wake time for a GPUunit, GPU frequency, ring frequency, CPU frequency, memory frequency,resolution, frame rate, sampling rate, cache configuration, dispatchwidth, resource cacheability settings, compiler optimization settings,thread group walk order for compute shaders, shading frequency, enablingor disabling hardware features, and power configuration/state.

Embodiments of each of the above graphics subsystem 925, profiler 926,configuration developer 927, neural network trainer 928, configurationengine 929, and other system components may be implemented in hardware,software, or any suitable combination thereof. For example, portions orall of the apparatus 920 may be implemented as part of the parallelprocessor 200, further configured with one or more aspects ofembodiments as described herein in connection with FIGS. 6A to 10C. Theapparatus 920 may also be adapted to work with a stereo head mountedsystem such as, for example, the system described in connection withFIGS. 11-15 below. For example, hardware implementations may includeconfigurable logic such as, for example, PLAs, FPGAs, CPLDs, or infixed-functionality logic hardware using circuit technology such as, forexample, ASIC, CMOS or TTL technology, or any combination thereof.Alternatively, or additionally, these components may be implemented inone or more modules as a set of logic instructions stored in a machine-or computer-readable storage medium such as RAM, ROM, PROM, firmware,flash memory, etc., to be executed by a processor or computing device.For example, computer program code to carry out the operations of thecomponents may be written in any combination of one or more operatingsystem applicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

Turning now to FIG. 9C, an embodiment of a method 930 of configuringgraphics may include determining profile information for a graphicsapplication at block 931, and developing a configuration decisionnetwork for the graphics application based on the profile information atblock 932. For example, the method 930 may include training a neuralnetwork to develop the configuration decision network for the graphicsapplication based on the profile information at block 933. Someembodiments of the method 930 may further include running the graphicsapplication at block 934, and adjusting a configuration of a graphicsoperation based on the configuration decision network at block 935.

At block 936, for example, the profile information may include one ormore of: application execution time, wake time of a GPU unit,application states, active shaders, constant values, execution time of aunit of work (e.g. a draw or render pass), cache hit/miss rate, registerpressure, memory usage, performance bottleneck, occupancy/busy-ness of ahardware block, and other hardware and software metrics.

At block 937, for example, the configuration decision network mayinclude a decision path for one or more of: sleep time for a GPU unit,wake time for a GPU unit, GPU frequency, ring frequency, CPU frequency,memory frequency, resolution, frame rate, sampling rate, cacheconfiguration, dispatch width, resource cacheability settings, compileroptimization settings, thread group walk order for compute shaders,shading frequency, enabling or disabling hardware features, and powerconfiguration/state.

Embodiments of the method 930 may be implemented in a system, apparatus,GPU, PPU, or a graphics processor pipeline apparatus such as, forexample, those described herein. More particularly, hardwareimplementations of the method 930 may include configurable logic suchas, for example, PLAs, FPGAs, CPLDs, or in fixed-functionality logichardware using circuit technology such as, for example, ASIC, CMOS, orTTL technology, or any combination thereof. Alternatively, oradditionally, the method 930 may be implemented in one or more modulesas a set of logic instructions stored in a machine- or computer-readablestorage medium such as RAM, ROM, PROM, firmware, flash memory, etc., tobe executed by a processor or computing device. For example, computerprogram code to carry out the operations of the components may bewritten in any combination of one or more operating systemapplicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. For example, the method 930 may be implemented on a computerreadable medium as described in connection with Examples 66 to 70 below.

For example, embodiments or portions of the method 930 may beimplemented in applications (e.g. through an API) or driver software.Other embodiments or portions of the method 930 may be implemented inspecialized code (e.g. shaders) to be executed on a GPU. Otherembodiments or portions of the method 930 may be implemented in fixedfunction logic or specialized hardware (e.g. in the GPU).

Turning now to FIG. 10A, an embodiment of a graphics apparatus 1000 mayinclude a graphics subsystem 1015 to run a graphics application, ametrics monitor 1016 communicatively coupled to the graphics subsystem1015 to determine metrics based on resource utilization of the graphicsapplication, a configuration adjuster 1017 communicatively coupled tothe graphics subsystem 1015 and the metrics monitor 1016 to apply aconfiguration decision network 1018 to the metrics to provide aconfiguration adjustment decision and to adjust a configuration of agraphics operation based on the configuration adjustment decision. Forexample, the graphics subsystem 1015 may include a render engine and theconfiguration adjuster 1017 may adjust various parameters of the renderengine.

For example, the configuration decision network 1018 may include adecision path for one or more of: sleep time for a GPU unit, wake timefor a GPU unit, GPU frequency, ring frequency, CPU frequency, memoryfrequency, resolution, frame rate, sampling rate, cache configuration,dispatch width, resource cacheability settings, compiler optimizationsettings, thread group walk order for compute shaders, shadingfrequency, enabling or disabling hardware features, and powerconfiguration/state.

Embodiments of each of the above graphics subsystem 1015, metricsmonitor 1016, configuration adjuster 1017, configuration decisionnetwork 1018, and other components may be implemented in hardware,software, or any suitable combination thereof. For example, portions orall of the apparatus 1000 may be implemented as part of the parallelprocessor 200, further configured with one or more aspects ofembodiments as described herein in connection with FIGS. 6A to 10C. Theapparatus 1000 may also be adapted to work with a stereo head mountedsystem such as, for example, the system described in connection withFIGS. 11-15 below. For example, hardware implementations may includeconfigurable logic such as, for example, PLAs, FPGAs, CPLDs, or infixed-functionality logic hardware using circuit technology such as, forexample, ASIC, CMOS or TTL technology, or any combination thereof.Alternatively, or additionally, these components may be implemented inone or more modules as a set of logic instructions stored in a machine-or computer-readable storage medium such as RAM, ROM, PROM, firmware,flash memory, etc., to be executed by a processor or computing device.For example, computer program code to carry out the operations of thecomponents may be written in any combination of one or more operatingsystem applicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages.

Turning now to FIG. 10B, an embodiment of a method 1030 of configuringgraphics may include running a graphics application at block 1031,determining metrics based on resource utilization of the graphicsapplication at block 1032, applying a configuration decision network tothe metrics to provide a configuration adjustment decision at block1033, and adjusting a configuration of a graphics operation based on theconfiguration adjustment decision at block 1034.

At block 1035, for example, the configuration decision network mayinclude a decision path for one or more of: sleep time for a GPU unit,wake time for a GPU unit, GPU frequency, ring frequency, CPU frequency,memory frequency, resolution, frame rate, sampling rate, cacheconfiguration, dispatch width, resource cacheability settings, compileroptimization settings, thread group walk order for compute shaders,shading frequency, enabling or disabling hardware features, and powerconfiguration/state.

Embodiments of the method 1030 may be implemented in a system,apparatus, GPU, PPU, or a graphics processor pipeline apparatus such as,for example, those described herein. More particularly, hardwareimplementations of the method 1030 may include configurable logic suchas, for example, PLAs, FPGAs, CPLDs, or in fixed-functionality logichardware using circuit technology such as, for example, ASIC, CMOS, orTTL technology, or any combination thereof. Alternatively, oradditionally, the method 1030 may be implemented in one or more modulesas a set of logic instructions stored in a machine- or computer-readablestorage medium such as RAM, ROM, PROM, firmware, flash memory, etc., tobe executed by a processor or computing device. For example, computerprogram code to carry out the operations of the components may bewritten in any combination of one or more operating systemapplicable/appropriate programming languages, including anobject-oriented programming language such as PYTHON, PERL, JAVA,SMALLTALK, C++, C# or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. For example, the method 1030 may be implemented on a computerreadable medium as described in connection with Examples 66 to 70 below.

For example, embodiments or portions of the method 1030 may beimplemented in applications (e.g. through an API) or driver software.Other embodiments or portions of the method 1030 may be implemented inspecialized code (e.g. shaders) to be executed on a GPU. Otherembodiments or portions of the method 1030 may be implemented in fixedfunction logic or specialized hardware (e.g. in the GPU).

A problem with complex graphics systems is that various devices may beconfigured differently along many different vectors. Those differentconfiguration vectors may affect the performance characteristics, whichmay affect how the applications run and what the user experience is. Thebest machine configuration may depend on the work that needs to beperformed. Advantageously, some embodiments may choose a better oroptimal configuration at a given point which is specific to the workloadthat is actually executing or that is about to execute. Some embodimentsmay predict how to configure the machine for the work that is coming upnext (e.g. when it is not necessarily known what that work is).

Advantageously, the configuration adjuster may look at a prior frame topredict work for a future frame. For example, based upon a previous 3Dframe behavior (e.g., workload characteristic and correlation betweenadjacent frames), the configuration adjuster may use the previous frameto determine an improved or optimal graphics logic configuration toimprove or maximize power/performance efficiency for future frames.Advantageously, configuration settings and/or efficiency may beimproved.

In some embodiments, the prior frame-based adjustment approach may allowsimpler decision making. The system may look at a past frame or data setand make a decision about a current or future configuration based onthat (e.g. if memory bandwidth is above a threshold, then increase ringfrequency). In some embodiments, utilization of different aspects of theGPU in the prior frame may be a good indicator of how the GPU will beused in a current frame (or a subsequent frame). For example, someembodiments may be particularly useful for 3D games where content maynot change much from one frame to next (e.g. same geometries, textures,etc.). In some embodiments, a profile may be developed offline on acompletely separate system, and the configuration information may beprovided to other systems with the same configuration (e.g. via thecloud).

Advantageously, some embodiments may provide machine learning forimproved or optimal machine configuration and efficiency. In the machinelearning case, for example, the configuration adjuster may look athistory information. For example, a profile for a graphics application(e.g. a video game) may be provided by the vendor or may be available todownload (e.g. from a cloud service). In addition, or alternatively, thesystem may include an application profiler which runs on the system todevelop or augment that information. The profile information may beprovided to a machine learning network to train it. The trained networkmay be referred to as a configuration decision network. During runtime,information about the workload that is running next may be provided tothe configuration decision network and the machine-learning process candetermine a better or optimal configuration for the machine.

For a graphics subsystem, the machine configuration settings may referto a variety of GPU parameters. These parameters include GPU/CPU/ringfrequency, power distributions (how much power to provide variouscomponents), cache configurations (how much cache to allocate to certaintypes of resources), dispatch width for various dispatch shaders,resolution, multi-sampling anti-aliasing (MSAA depth), etc.Advantageously, some embodiments may control a large set of machineconfiguration parameters based on machine-learning and/or historicalinformation. Numerous machines, systems, apparatuses, module, units, andcircuit may implement various aspects of the embodiments, including aHMD, a VR system, or other graphics systems. Advantageously, someembodiments may provide better machine configuration and efficiency.

Turning now to FIG. 10C, an example of a method 1050 of using machinelearning to configure a machine may include profiling an application atblock 1051, varying the unit wake times at 1052 (e.g. to measureapplication execution times, wake times of GPU units (slices/subslices), etc., during profiling), and then use the collected information(e.g. execution times, application states, active shaders, constants,etc.) as input for a neural network (NN) training at block 1053. Themethod 1050 may then run the full application (e.g. by the user atruntime) at block 1054 and use the trained neural network to choose waketimes for GPU units and perform frequency management of subsystems(e.g., GPU, ring, CPU, Memory) at block 1055.

The initial profile information may be retrieved and/or developed from anumber of sources. For example, a benchmark mode of the application mayprovide profile information. There may be pre-stored (e.g. cloud)profile information for the application. During run-time, a profile mayanalyze usage over the past N (e.g. N=20) frames to develop profileinformation. In the prior frame-based example, the prior frame providesthe profile information. The system may then take the profile data andtry different configurations of the machine. For example, the system mayrun the set of profile data on different configurations and settings(e.g. use a different number of GPU slices, use a different number ofsub-slices, etc.) and measure how the machine did for eachconfiguration. All of the results may be fed into a neural network. Theneural network may be trained and the neural network may then provide adecision tree which may be used during runtime to determine the machineconfigurations.

For example, when the application is run and the workload may bemonitored and metrics may be applied to the decision tree. For example,the neural network may have learned based on past monitoring that whenan application demands a large amount of memory bandwidth performanceincreases if the frequency of the ring is increased. So if the currentworkload is using a large amount of memory bandwidth, a machineconfiguration may be selected that accommodates that and yields betteror optimal results. This may be applied across many or all of thecontrollable parameters. In some cases, the effects of configurationadjustments from the neural network may be predictive in nature.

Inputs to the neural networks may include measured hardware countersassociated with the frames that are being profiled, and the selectedconfiguration that generated those hardware counter values. These inputsare provided to the neural network to train it. The neural network mayoutput a decision tree that says these particular hardware metrics arethe ones that indicate what configuration to choose. During runtime, theconfiguration adjuster may walk the decision tree to decide whatconfiguration to choose for the next frame in real time based on whatwere the measured counter values for the last frame (or predicted valuesfor a next workload).

Training the neural network may be iterative process (e.g. where newcounter values and configuration information are regularly provided tothe neural network for refining, e.g. every 5 minutes, or 50 frames,etc.). Alternatively, the decision tree may be relatively static for aparticular user/app/context that has been profiled. Embodiments of theconfiguration adjuster may be implemented at a driver level, whileprofiling may be done at an application level or OS level.

Head-Mounted Integrated Interface System Overview

FIG. 11 shows a head mounted display (HMD) system 1100 that is beingworn by a user while experiencing an immersive environment such as, forexample, a virtual reality (VR) environment, an augmented reality (AR)environment, a multi-player three-dimensional (3D) game, and so forth.In the illustrated example, one or more straps 1120 hold a frame 1102 ofthe HMD system 1100 in front of the eyes of the user. Accordingly, aleft-eye display 1104 may be positioned to be viewed by the left eye ofthe user and a right-eye display 1106 may be positioned to be viewed bythe right eye of the user. The left-eye display 1104 and the right-eyedisplay 1106 may alternatively be integrated into a single display incertain examples such as, for example, a smart phone being worn by theuser. In the case of AR, the displays 1104, 1106 may be view-throughdisplays that permit the user to view the physical surroundings, withother rendered content (e.g., virtual characters, informationalannotations, heads up display/HUD) being presented on top a live feed ofthe physical surroundings.

In one example, the frame 1102 includes a left look-down camera 1108 tocapture images from an area generally in front of the user and beneaththe left eye (e.g., left hand gestures). Additionally, a right look-downcamera 1110 may capture images from an area generally in front of theuser and beneath the right eye (e.g., right hand gestures). Theillustrated frame 1102 also includes a left look-front camera 1112 and aright look-front camera 1114 to capture images in front of the left andright eyes, respectively, of the user. The frame 1102 may also include aleft look-side camera 1116 to capture images from an area to the left ofthe user and a right look-side camera 1118 to capture images from anarea to the right of the user.

The images captured by the cameras 1108, 1110, 1112, 1114, 1116, 1118,which may have overlapping fields of view, may be used to detectgestures made by the user as well as to analyze and/or reproduce theexternal environment on the displays 1104, 1106. In one example, thedetected gestures are used by a graphics processing architecture (e.g.,internal and/or external) to render and/or control a virtualrepresentation of the user in a 3D game. Indeed, the overlapping fieldsof view may enable the capture of gestures made by other individuals(e.g., in a multi-player game), where the gestures of other individualsmay be further used to render/control the immersive experience. Theoverlapping fields of view may also enable the HMD system 1100 toautomatically detect obstructions or other hazards near the user. Suchan approach may be particularly advantageous in advanced driverassistance system (ADAS) applications.

In one example, providing the left look-down camera 1108 and the rightlook-down camera 1110 with overlapping fields of view provides astereoscopic view having an increased resolution. The increasedresolution may in turn enable very similar user movements to bedistinguished from one another (e.g., at sub-millimeter accuracy). Theresult may be an enhanced performance of the HMD system 1100 withrespect to reliability. Indeed, the illustrated solution may be usefulin a wide variety of applications such as, for example, coloringinformation in AR settings, exchanging virtual tools/devices betweenusers in a multi-user environment, rendering virtual items (e.g.,weapons, swords, staffs), and so forth. Gestures of other objects, limbsand/or body parts may also be detected and used to render/control thevirtual environment. For example, myelographic signals,electroencephalographic signals, eye tracking, breathing or puffing,hand motions, etc., may be tracked in real-time, whether from the weareror another individual in a shared environment. The images captured bythe cameras 1108, 1110, 1112, 1114, 1116, 1118, may also serve ascontextual input. For example, it might be determined that the user isindicating a particular word to edit or key to press in a wordprocessing application, a particular weapon to deployed or a traveldirection in a game, and so forth.

Additionally, the images captured by the cameras 1108, 1110, 1112, 1114,1116, 1118, may be used to conduct shared communication or networkedinteractivity in equipment operation, medical training, and/orremote/tele-operation guidance applications. Task specific gesturelibraries or neural network machine learning could enable toolidentification and feedback for a task. For example, a virtual tool thattranslates into remote, real actions may be enabled. In yet anotherexample, the HMD system 1100 translates the manipulation of a virtualdrill within a virtual scene to the remote operation of a drill on arobotic device deployed to search a collapsed building. Moreover, theHMD system 1100 may be programmable to the extent that it includes, forexample, a protocol that enables the user to add a new gesture to a listof identifiable gestures associated with user actions.

In addition, the various cameras in the HMD 1100 may be configurable todetect spectrum frequencies in addition to the visible wavelengths ofthe spectrum. Multi-spectral imaging capabilities in the input camerasallows position tracking of the user and/or objects by eliminatingnonessential image features (e.g., background noise). For example, inaugmented reality (AR) applications such as surgery, instruments andequipment may be tracked by their infrared reflectivity without the needfor additional tracking aids. Moreover, HMD 1100 could be employed insituations of low visibility where a “live feed” from the variouscameras could be enhanced or augmented through computer analysis anddisplayed to the user as visual or audio cues.

The HMD system 1100 may also forego performing any type of datacommunication with a remote computing system or need power cables (e.g.,independent mode of operation). In this regard, the HMD system 1100 maybe a “cordless” device having a power unit that enables the HMD system1100 to operate independently of external power systems. Accordingly,the user might play a full featured game without being tethered toanother device (e.g., game console) or power supply. In a wordprocessing example, the HMD system 1100 might present a virtual keyboardand/or virtual mouse on the displays 1104 and 1106 to provide a virtualdesktop or word processing scene. Thus, gesture recognition datacaptured by one or more of the cameras may represent user typingactivities on the virtual keyboard or movements of the virtual mouse.Advantages include, but are not limited to, ease of portability andprivacy of the virtual desktop from nearby individuals. The underlyinggraphics processing architecture may support compression and/ordecompression of video and audio signals. Moreover, providing separateimages to the left eye and right eye of the user may facilitate therendering, generation and/or perception of 3D scenes. The relativepositions of the left-eye display 1104 and the right-eye display 1106may also be adjustable to match variations in eye separation betweendifferent users.

The number of cameras illustrated in FIG. 11 is to facilitate discussiononly. Indeed, the HMD system 1100 may include less than six or more thansix cameras, depending on the circumstances.

Functional Components of the HMD System

FIG. 12 shows the HMD system in greater detail. In the illustratedexample, the frame 1102 includes a power unit 1200 (e.g., battery power,adapter) to provide power to the HMD system. The illustrated frame 1102also includes a motion tracking module 1220 (e.g., accelerometers,gyroscopes), wherein the motion tracking module 1220 provides motiontracking data, orientation data and/or position data to a processorsystem 1204. The processor system 1204 may include a network adapter1224 that is coupled to an I/O bridge 1206. The I/O bridge 1206 mayenable communications between the network adapter 1224 and variouscomponents such as, for example, audio input modules 1210, audio outputmodules 1208, a display device 1207, input cameras 1202, and so forth.

In the illustrated example, the audio input modules 1210 include aright-audio input 1218 and a left-audio input 1216, which detect soundthat may be processed in order to recognize voice commands of the useras well as nearby individuals. The voice commands recognized in thecaptured audio signals may augment gesture recognition during modalityswitching and other applications. Moreover, the captured audio signalsmay provide 3D information that is used to enhance the immersiveexperience.

The audio output modules 1208 may include a right-audio output 1214 anda left-audio output 1212. The audio output modules 1208 may deliversound to the ears of the user and/or other nearby individuals. The audiooutput modules 1208, which may be in the form of earbuds, on-earspeakers, over the ear speakers, loudspeakers, etc., or any combinationthereof, may deliver stereo and/or 3D audio content to the user (e.g.,spatial localization). The illustrated frame 1102 also includes awireless module 1222, which may facilitate communications between theHMD system and various other systems (e.g., computers, wearable devices,game consoles). In one example, the wireless module 1222 communicateswith the processor system 1204 via the network adapter 1224.

The illustrated display device 1207 includes the left-eye display 1104and the right-eye display 1106, wherein the visual content presented onthe displays 1104, 1106 may be obtained from the processor system 1204via the I/O bridge 1206. The input cameras 1202 may include the leftlook-side camera 1116 the right look-side camera 1118, the leftlook-down camera 1108, the left look-front camera 1112, the rightlook-front camera 1114 and the right look-down camera 1110, alreadydiscussed.

Turning now FIG. 13, a general processing cluster (GPC) 1300 is shown.The illustrated GPC 1300 may be incorporated into a processing systemsuch as, for example, the processor system 1204 (FIG. 12), alreadydiscussed. The GPC 1300 may include a pipeline manager 1302 thatcommunicates with a scheduler. In one example, the pipeline manager 1302receives tasks from the scheduler and distributes the tasks to one ormore streaming multi-processors (SM's) 1304. Each SM 1304 may beconfigured to process thread groups, wherein a thread group may beconsidered a plurality of related threads that execute the same orsimilar operations on different input data. Thus, each thread in thethread group may be assigned to a particular SM 1304. In anotherexample, the number of threads may be greater than the number ofexecution units in the SM 1304. In this regard, the threads of a threadgroup may operate in parallel. The pipeline manager 1302 may alsospecify processed data destinations to a work distribution crossbar1308, which communicates with a memory crossbar.

Thus, as each SM 1304 transmits a processed task to the workdistribution crossbar 1308, the processed task may be provided toanother GPC 1300 for further processing. The output of the SM 1304 mayalso be sent to a pre-raster operations (preROP) unit 1314, which inturn directs data to one or more raster operations units, or performsother operations (e.g., performing address translations, organizingpicture color data, blending color, and so forth). The SM 1304 mayinclude an internal level one (L1) cache (not shown) to which the SM1304 may store data. The SM 1304 may also have access to a level two(L2) cache (not shown) via a memory management unit (MMU) 1310 and alevel one point five (L1.5) cache 1306. The MMU 1310 may map virtualaddresses to physical addresses. In this regard, the MMU 1310 mayinclude page table entries (PTE's) that are used to map virtualaddresses to physical addresses of a tile, memory page and/or cache lineindex. The illustrated GPC 1300 also includes a texture unit 1312.

Graphics Pipeline Architecture

Turning now to FIG. 14, a graphics pipeline 1400 is shown. In theillustrated example, a world space pipeline 1420 includes a primitivedistributor (PD) 1402. The PD 1402 may collect vertex data associatedwith high-order services, graphics primitives, triangles, etc., andtransmit the vertex data to a vertex attribute fetch unit (VAF) 1404.The VAF 1404 may retrieve vertex attributes associated with each of theincoming vertices from shared memory and store the vertex data, alongwith the associated vertex attributes, into shared memory.

The illustrated world space pipeline 1420 also includes a vertex,tessellation, geometry processing unit (VTG) 1406. The VTG 1406 mayinclude, for example, a vertex processing unit, a tessellationinitialization processing unit, a task distributor, a task generationunit, a topology generation unit, a geometry processing unit, atessellation processing unit, etc., or any combination thereof. In oneexample, the VTG 1406 is a programmable execution unit that isconfigured to execute geometry programs, tessellation programs, andvertex shader programs. The programs executed by the VTG 1406 mayprocess the vertex data and vertex attributes received from the VAF1404. Moreover, the programs executed by the VTG 1406 may producegraphics primitives, color values, surface normal factors andtransparency values at each vertex for the graphics primitives forfurther processing within the graphics processing pipeline 1400.

The vertex processing unit of the VTG 1406 may be a programmableexecution unit that executes vertex shader programs, lighting andtransforming vertex data as specified by the vertex shader programs. Forexample, the vertex processing unit might be programmed to transform thevertex data from an object-based coordinate representation (e.g. objectspace) to an alternatively based coordinate system such as world spaceor normalize device coordinates (NDC) space. Additionally, the vertexprocessing unit may read vertex data and vertex attributes that arestored in shared memory by the VAF 1404 and process the vertex data andvertex attributes. In one example, the vertex processing unit storesprocessed vertices in shared memory.

The tessellation initialization processing unit (e.g., hull shader,tessellation control shader) may execute tessellation initializationshader programs. In one example, the tessellation initializationprocessing unit processes vertices produced by the vertex processingunit and generates graphics primitives sometimes referred to as“patches”. The tessellation initialization processing unit may alsogenerate various patch attributes, wherein the patch data and the patchattributes are stored to shared memory. The task generation unit of theVTG 1406 may retrieve data and attributes for vertices and patches fromshared memory. In one example, the task generation unit generates tasksfor processing the vertices and patches for processing by the laterstages in the graphics processing pipeline 1400.

The tasks produced by the task generation unit may be redistributed bythe task distributor of the VTG 1406. For example, the tasks produced bythe various instances of the vertex shader program and the tessellationinitialization program may vary significantly between one graphicsprocessing pipeline 1400 and another. Accordingly, the task distributormay redistribute these tasks such that each graphics processing pipeline1400 has approximately the same workload during later pipeline stages.

As already noted, the VTG 1406 may also include a topology generationunit. In one example, the topology generation unit retrieves tasksdistributed by the task distributor, indexes the vertices, includingvertices associated with patches, and computes coordinates (UV) fortessellation vertices and the indices that connect the tessellationvertices to form graphics primitives. The indexed vertices may be storedby the topology generation unit in shared memory. The tessellationprocessing unit of the VTG 1406 may be configured to executetessellation shader programs (e.g., domain shaders, tessellationevaluation shaders). The tessellation processing unit may read inputdata from shared memory and write output data to shared memory. Theoutput data may be passed from the shared memory to the geometryprocessing unit (e.g., the next shader stage) as input data.

The geometry processing unit of the VTG 1406 may execute geometry shaderprograms to transform graphics primitives (e.g., triangles, linesegments, points, etc.). In one example, vertices are grouped toconstruct graphics primitives, wherein the geometry processing unitsubdivides the graphics primitives into one or more new graphicsprimitives. The geometry processing unit may also calculate parameterssuch as, for example, plain equation coefficients, that may be used torasterize the new graphics primitives.

The illustrated world space pipeline 1420 also includes a viewportscale, cull, and clip unit (VPC) 1408 that receives the parameters andvertices specifying new graphics primitives from the VTG 1406. In oneexample, the VPC 1408 performs clipping, cuffing, perspectivecorrection, and viewport transformation to identify the graphicsprimitives that are potentially viewable in the final rendered image.The VPC 1408 may also identify the graphics primitives that may not beviewable.

The graphics processing pipeline 1400 may also include a tiling unit1410 coupled to the world space pipeline 1420. The tiling unit 1410 maybe a graphics primitive sorting engine, wherein graphics primitives areprocessed in the world space pipeline 1420 and then transmitted to thetiling unit 1410. In this regard, the graphics processing pipeline 1400may also include a screen space pipeline 1422, wherein the screen spacemay be divided into cache tiles. Each cache tile may therefore beassociated with a portion of the screen space. For each graphicsprimitive, the tiling unit 1410 may identify the set of cache tiles thatintersect with the graphics primitive (e.g. “tiling”). After tiling anumber of graphics primitives, the tiling unit 1410 may process thegraphics primitives on a cache tile basis. In one example, graphicsprimitives associated with a particular cache tile are transmitted to asetup unit 1412 in the screen space pipeline 1422 one tile at a time.Graphics primitives that intersect with multiple cache tiles may beprocessed once in the world space pipeline 1420, while being transmittedmultiple times to the screen space pipeline 1422.

In one example, the setup unit 1412 receives vertex data from the VPC1408 via the tiling unit 1410 and calculates parameters associated withthe graphics primitives. The parameters may include, for example, edgeequations, partial plane equations, and depth plain equations. Thescreen space pipeline 1422 may also include a rasterizer 1414 coupled tothe setup unit 1412. The rasterizer may scan convert the new graphicsprimitives and transmit fragments and coverage data to a pixel shadingunit (PS) 1416. The rasterizer 1414 may also perform Z culling and otherZ-based optimizations.

The PS 1416, which may access shared memory, may execute fragment shaderprograms that transform fragments received from the rasterizer 1414.More particularly, the fragment shader programs may shade fragments atpixel-level granularity (e.g., functioning as pixel shader programs). Inanother example, the fragment shader programs shade fragments atsample-level granularity, where each pixel includes multiple samples,and each sample represents a portion of a pixel. Moreover, the fragmentshader programs may shade fragments at any other granularity, dependingon the circumstances (e.g., sampling rate). The PS 1416 may performblending, shading, perspective correction, texture mapping, etc., togenerate shaded fragments.

The illustrated screen space pipeline 1422 also includes a rasteroperations unit (ROP) 1418, which may perform raster operations such as,for example, stenciling, Z-testing, blending, and so forth. The ROP 1418may then transmit pixel data as processed graphics data to one or morerendered targets (e.g., graphics memory). The ROP 1418 may be configuredto compress Z or color data that is written to memory and decompress Zor color data that is read from memory. The location of the ROP 1418 mayvary depending on the circumstances.

The graphics processing pipeline 1400 may be implemented by one or moreprocessing elements. For example, the VTG 1406 and/or the PS 1416 may beimplemented in one or more SM's, the PD 1402, the VAF 1404, the VPC1408, the tiling unit 1410, the setup unit 1412, the rasterizer 1414and/or the ROP 1418 might be implemented in processing elements of aparticular GPC in conjunction with a corresponding partition unit. Thegraphics processing pipeline 1400 may also be implemented infixed-functionality hardware logic. Indeed, the graphics processingpipeline 1400 may be implemented in a PPU.

Thus, the illustrated world space pipeline 1420 processes graphicsobjects in 3D space, where the position of each graphics object is knownrelative to other graphics objects and relative to a 3D coordinatesystem. By contrast, the screen space pipeline 1422 may process graphicsobjects that have been projected from the 3D coordinate system onto a 2Dplanar surface that represents the surface of the display device.Additionally, the world space pipeline 1420 may be divided into an alphaphase pipeline and a beta phase pipeline, wherein the alpha phasepipeline includes pipeline stages from the PD 1402 through the taskgeneration unit. The beta phase pipeline might include pipeline stagesfrom the topology generation unit through the VPC 1408. In such a case,the graphics processing pipeline 1400 may perform a first set ofoperations (e.g., a single thread, a thread group, multiple threadgroups acting in unison) in the alpha phase pipeline and a second set ofoperations (e.g., a single thread, a thread group, multiple threadgroups acting in unison) in the beta phase pipeline.

If multiple graphics processing pipelines 1400 are in use, the vertexdata and vertex attributes associated with a set of graphics objects maybe divided so that each graphics processing pipeline 1400 has a similarworkload through the alpha phase. Accordingly, alpha phase processingmay substantially expand the amount of vertex data and vertexattributes, such that the amount of vertex data and vertex attributesproduced by the task generation unit is significantly larger than theamount of vertex data and vertex attributes processed by the PD 1402 andthe VAF 1404. Moreover, the task generation units associated withdifferent graphics processing pipelines 1400 may produce vertex data andvertex attributes having different levels of quality, even whenbeginning the alpha phase with the same quantity of attributes. In suchcases, the task distributor may redistribute the attributes produced bythe alpha phase pipeline so that each graphics processing pipeline 1400has approximately the same workload at the beginning of the beta phasepipeline.

Turning now to FIG. 15, a streaming multi-processor (SM) 1500 is shown.The illustrated SM 1500 includes K scheduler units 1504 coupled to aninstruction cache 1502, wherein each scheduler unit 1504 receives athread block array from a pipeline manager (not shown) and managesinstruction scheduling for one or more thread blocks of each activethread block array. The scheduler unit 1504 may schedule threads forexecution in groups of parallel threads, where each group may bereferred to as a “warp”. Thus, each warp might include, for example,sixty-four threads. Additionally, the scheduler unit 1504 may manage aplurality of different thread blocks, allocating the thread blocks towarps for execution. The scheduler unit may then schedule instructionsfrom the plurality of different warps on various functional units duringeach clock cycle. Each scheduler unit 1504 may include one or moreinstructions dispatch units 1522, wherein each dispatch unit 1522transmits instructions to one or more of the functional units. Thenumber of dispatch units 1522 may vary depending on the circumstances.In the illustrated example, the scheduler unit 1504 includes twodispatch units 1522 that enable two different instructions from the samewarp to be dispatched during each clock cycle.

The SM 1500 may also include a register file 1506. The register file1506 may include a set of registers that are divided between thefunctional units such that each functional unit is allocated a dedicatedportion of the register file 1506. The register file 1506 may also bedivided between different warps being executed by the SM 1500. In oneexample the register file 1506 provides temporary storage for operandsconnected to the data paths of the functional units. The illustrated SM1500 also includes L processing cores 1508, wherein L may be arelatively large number (e.g., 192). Each core 1508 may be a pipelined,single-precision processing unit that includes a floating pointarithmetic logic unit (e.g., IEEE 754-2008) as well as an integerarithmetic logic unit.

The illustrated SM 1500 also includes M double precision units (DPU's)1510, N special function units (SFU's) 1512 and P load/store units(LSU's) 1514. Each DPU 1510 may implement double-precision floatingpoint arithmetic and each SFU 1512 may perform special functions suchas, for example, rectangle copying pixel blending, etc. Additionally,each LSU 1514 may conduct load and store operations between a sharedmemory 1518 and the register file 1506. In one example, the load andstore operations are conducted through J texture unit/L1 caches 1520 andan interconnected network 1516. In one example, the J texture unit/L1caches 1520 are also coupled to a crossbar (not shown). Thus, theinterconnect network 1516 may connect each of the functional units tothe register file 1506 and to the shared memory 1518. In one example,the interconnect network 1516 functions as a crossbar that connects anyof the functional units to any of the registers in the register file1506.

The SM 1500 may be implemented within a graphics processor (e.g.,graphics processing unit/GPU), wherein the texture unit/L1 caches 1520may access texture maps from memory and sample the texture maps toproduce sampled texture values for use in shader programs. Textureoperations performed by the texture unit/L1 caches 1520 include, but arenot limited to, antialiasing based on mipmaps.

Additional System Overview Example

FIG. 16 is a block diagram of a processing system 1600, according to anembodiment. In various embodiments the system 1600 includes one or moreprocessors 1602 and one or more graphics processors 1608, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1602 or processorcores 1607. In on embodiment, the system 1600 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1600 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1600 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1600 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1600 is a television or set topbox device having one or more processors 1602 and a graphical interfacegenerated by one or more graphics processors 1608.

In some embodiments, the one or more processors 1602 each include one ormore processor cores 1607 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1607 is configured to process aspecific instruction set 1609. In some embodiments, instruction set 1609may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1607 may each processa different instruction set 1609, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1607may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1602 includes cache memory 1604.Depending on the architecture, the processor 1602 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1602. In some embodiments, the processor 1602 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1607 using knowncache coherency techniques. A register file 1606 is additionallyincluded in processor 1602 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1602.

In some embodiments, processor 1602 is coupled to a processor bus 1610to transmit communication signals such as address, data, or controlsignals between processor 1602 and other components in system 1600. Inone embodiment the system 1600 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1616 and an Input Output(I/O) controller hub 1630. A memory controller hub 1616 facilitatescommunication between a memory device and other components of system1600, while an I/O Controller Hub (ICH) 1630 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1616 is integrated within the processor.

Memory device 1620 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 1620 can operate as system memory for the system 1600, to storedata 1622 and instructions 1621 for use when the one or more processors1602 executes an application or process. Memory controller hub 1616 alsocouples with an optional external graphics processor 1612, which maycommunicate with the one or more graphics processors 1608 in processors1602 to perform graphics and media operations.

In some embodiments, ICH 1630 enables peripherals to connect to memorydevice 1620 and processor 1602 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1646, afirmware interface 1628, a wireless transceiver 1626 (e.g., Wi-Fi,Bluetooth), a data storage device 1624 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1640 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1642 connect input devices, suchas keyboard and mouse 1644 combinations. A network controller 1634 mayalso couple to ICH 1630. In some embodiments, a high-performance networkcontroller (not shown) couples to processor bus 1610. It will beappreciated that the system 1600 shown is exemplary and not limiting, asother types of data processing systems that are differently configuredmay also be used. For example, the I/O controller hub 1630 may beintegrated within the one or more processor 1602, or the memorycontroller hub 1616 and I/O controller hub 1630 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1612.

FIG. 17 is a block diagram of an embodiment of a processor 1700 havingone or more processor cores 1702A-1702N, an integrated memory controller1714, and an integrated graphics processor 1708. Those elements of FIG.17 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1700 can include additional cores up to and including additional core1702N represented by the dashed lined boxes. Each of processor cores1702A-1702N includes one or more internal cache units 1704A-1704N. Insome embodiments each processor core also has access to one or moreshared cached units 1706.

The internal cache units 1704A-1704N and shared cache units 1706represent a cache memory hierarchy within the processor 1700. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1706 and1704A-1704N.

In some embodiments, processor 1700 may also include a set of one ormore bus controller units 1716 and a system agent core 1710. The one ormore bus controller units 1716 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1710 provides management functionality forthe various processor components. In some embodiments, system agent core1710 includes one or more integrated memory controllers 1714 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1702A-1702Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1710 includes components for coordinating andoperating cores 1702A-1702N during multi-threaded processing. Systemagent core 1710 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 1702A-1702N and graphics processor 1708.

In some embodiments, processor 1700 additionally includes graphicsprocessor 1708 to execute graphics processing operations. In someembodiments, the graphics processor 1708 couples with the set of sharedcache units 1706, and the system agent core 1710, including the one ormore integrated memory controllers 1714. In some embodiments, a displaycontroller 1711 is coupled with the graphics processor 1708 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1711 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1708 or system agent core 1710.

In some embodiments, a ring based interconnect unit 1712 is used tocouple the internal components of the processor 1700. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 1708 couples with the ring interconnect 1712 via an I/O link1713.

The exemplary I/O link 1713 represents at least one of multiplevarieties of I/O interconnects, including an on package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1718, such as an eDRAM module.In some embodiments, each of the processor cores 1702-1702N and graphicsprocessor 1708 use embedded memory modules 1718 as a shared Last LevelCache.

In some embodiments, processor cores 1702A-1702N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1702A-1702N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1702A-Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1702A-1702N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1700 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 18 is a block diagram of a graphics processor 1800, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 1800 includesa memory interface 1814 to access memory. Memory interface 1814 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 1800 also includes a displaycontroller 1802 to drive display output data to a display device 1820.Display controller 1802 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 1800includes a video codec engine 1806 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1800 includes a block imagetransfer (BLIT) engine 1804 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 1810. In someembodiments, graphics processing engine 1810 is a compute engine forperforming graphics operations, including three-dimensional (3D)graphics operations and media operations.

In some embodiments, GPE 1810 includes a 3D pipeline 1812 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 1812 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 1815.While 3D pipeline 1812 can be used to perform media operations, anembodiment of GPE 1810 also includes a media pipeline 1816 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 1816 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 1806. In some embodiments, media pipeline 1816 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 1815. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 1815.

In some embodiments, 3D/Media subsystem 1815 includes logic forexecuting threads spawned by 3D pipeline 1812 and media pipeline 1816.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 1815, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 1815 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

3D/Media Processing

FIG. 19 is a block diagram of a graphics processing engine 1910 of agraphics processor in accordance with some embodiments. In oneembodiment, the GPE 1910 is a version of the GPE 1810 shown in FIG. 18.Elements of FIG. 19 having the same reference numbers (or names) as theelements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, GPE 1910 couples with a command streamer 1903,which provides a command stream to the GPE 3D and media pipelines 1912,1916. In some embodiments, command streamer 1903 is coupled to memory,which can be system memory, or one or more of internal cache memory andshared cache memory. In some embodiments, command streamer 1903 receivescommands from the memory and sends the commands to 3D pipeline 1912and/or media pipeline 1916. The commands are directives fetched from aring buffer, which stores commands for the 3D and media pipelines 1912,1916. In one embodiment, the ring buffer can additionally include batchcommand buffers storing batches of multiple commands. The 3D and mediapipelines 1912, 1916 process the commands by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to an execution unit array 1914. In some embodiments,execution unit array 1914 is scalable, such that the array includes avariable number of execution units based on the target power andperformance level of GPE 1910.

In some embodiments, a sampling engine 1930 couples with memory (e.g.,cache memory or system memory) and execution unit array 1914. In someembodiments, sampling engine 1930 provides a memory access mechanism forexecution unit array 1914 that allows execution array 1914 to readgraphics and media data from memory. In some embodiments, samplingengine 1930 includes logic to perform specialized image samplingoperations for media.

In some embodiments, the specialized media sampling logic in samplingengine 1930 includes a de-noise/de-interlace module 1932, a motionestimation module 1934, and an image scaling and filtering module 1936.In some embodiments, de-noise/de-interlace module 1932 includes logic toperform one or more of a de-noise or a de-interlace algorithm on decodedvideo data. The de-interlace logic combines alternating fields ofinterlaced video content into a single fame of video. The de-noise logicreduces or removes data noise from video and image data. In someembodiments, the de-noise logic and de-interlace logic are motionadaptive and use spatial or temporal filtering based on the amount ofmotion detected in the video data. In some embodiments, thede-noise/de-interlace module 1932 includes dedicated motion detectionlogic (e.g., within the motion estimation engine 1934).

In some embodiments, motion estimation engine 1934 provides hardwareacceleration for video operations by performing video accelerationfunctions such as motion vector estimation and prediction on video data.The motion estimation engine determines motion vectors that describe thetransformation of image data between successive video frames. In someembodiments, a graphics processor media codec uses video motionestimation engine 1934 to perform operations on video at the macro-blocklevel that may otherwise be too computationally intensive to performwith a general-purpose processor. In some embodiments, motion estimationengine 1934 is generally available to graphics processor components toassist with video decode and processing functions that are sensitive oradaptive to the direction or magnitude of the motion within video data.

In some embodiments, image scaling and filtering module 1936 performsimage-processing operations to enhance the visual quality of generatedimages and video. In some embodiments, scaling and filtering module 1936processes image and video data during the sampling operation beforeproviding the data to execution unit array 1914.

In some embodiments, the GPE 1910 includes a data port 1944, whichprovides an additional mechanism for graphics subsystems to accessmemory. In some embodiments, data port 1944 facilitates memory accessfor operations including render target writes, constant buffer reads,scratch memory space reads/writes, and media surface accesses. In someembodiments, data port 1944 includes cache memory space to cacheaccesses to memory. The cache memory can be a single data cache orseparated into multiple caches for the multiple subsystems that accessmemory via the data port (e.g., a render buffer cache, a constant buffercache, etc.). In some embodiments, threads executing on an executionunit in execution unit array 1914 communicate with the data port byexchanging messages via a data distribution interconnect that coupleseach of the sub-systems of GPE 1910.

Execution Units

FIG. 20 is a block diagram of another embodiment of a graphics processor2000. Elements of FIG. 20 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2000 includes a ringinterconnect 2002, a pipeline front-end 2004, a media engine 2037, andgraphics cores 2080A-2080N. In some embodiments, ring interconnect 2002couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2000 receives batches ofcommands via ring interconnect 2002. The incoming commands areinterpreted by a command streamer 2003 in the pipeline front-end 2004.In some embodiments, graphics processor 2000 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2080A-2080N. For 3D geometry processing commands,command streamer 2003 supplies commands to geometry pipeline 2036. Forat least some media processing commands, command streamer 2003 suppliesthe commands to a video front end 2034, which couples with a mediaengine 2037. In some embodiments, media engine 2037 includes a VideoQuality Engine (VQE) 2030 for video and image post-processing and amulti-format encode/decode (MFX) 2033 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2036 and media engine 2037 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2080A.

In some embodiments, graphics processor 2000 includes scalable threadexecution resources featuring modular cores 2080A-2080N (sometimesreferred to as core slices), each having multiple sub-cores 2050A-2050N,2060A-2060N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2000 can have any number of graphicscores 2080A through 2080N. In some embodiments, graphics processor 2000includes a graphics core 2080A having at least a first sub-core 2050Aand a second core sub-core 2060A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2050A).In some embodiments, graphics processor 2000 includes multiple graphicscores 2080A-2080N, each including a set of first sub-cores 2050A-2050Nand a set of second sub-cores 2060A-2060N. Each sub-core in the set offirst sub-cores 2050A-2050N includes at least a first set of executionunits 2052A-2052N and media/texture samplers 2054A-2054N. Each sub-corein the set of second sub-cores 2060A-2060N includes at least a secondset of execution units 2062A-2062N and samplers 2064A-2064N. In someembodiments, each sub-core 2050A-2050N, 2060A-2060N shares a set ofshared resources 2070A-2070N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

FIG. 21 illustrates thread execution logic 2100 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 21 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 2100 includes a pixel shader2102, a thread dispatcher 2104, instruction cache 2106, a scalableexecution unit array including a plurality of execution units2108A-2108N, a sampler 2110, a data cache 2112, and a data port 2114. Inone embodiment the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 2100 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 2106, data port 2114, sampler 2110, andexecution unit array 2108A-2108N. In some embodiments, each executionunit (e.g. 2108A) is an individual vector processor capable of executingmultiple simultaneous threads and processing multiple data elements inparallel for each thread. In some embodiments, execution unit array2108A-2108N includes any number individual execution units.

In some embodiments, execution unit array 2108A-2108N is primarily usedto execute “shader” programs. In some embodiments, the execution unitsin array 2108A-2108N execute an instruction set that includes nativesupport for many standard 3D graphics shader instructions, such thatshader programs from graphics libraries (e.g., Direct 3D and OpenGL) areexecuted with a minimal translation. The execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders).

Each execution unit in execution unit array 2108A-2108N operates onarrays of data elements. The number of data elements is the “executionsize,” or the number of channels for the instruction. An executionchannel is a logical unit of execution for data element access, masking,and flow control within instructions. The number of channels may beindependent of the number of physical Arithmetic Logic Units (ALUs) orFloating Point Units (FPUs) for a particular graphics processor. In someembodiments, execution units 2108A-2108N support integer andfloating-point data types.

The execution unit instruction set includes single instruction multipledata (SIMD) instructions. The various data elements can be stored as apacked data type in a register and the execution unit will process thevarious elements based on the data size of the elements. For example,when operating on a 256-bit wide vector, the 256 bits of the vector arestored in a register and the execution unit operates on the vector asfour separate 64-bit packed data elements (Quad-Word (QW) size dataelements), eight separate 32-bit packed data elements (Double Word (DW)size data elements), sixteen separate 16-bit packed data elements (Word(W) size data elements), or thirty-two separate 8-bit data elements(byte (B) size data elements). However, different vector widths andregister sizes are possible.

One or more internal instruction caches (e.g., 2106) are included in thethread execution logic 2100 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2112) are included to cache thread data during thread execution. In someembodiments, sampler 2110 is included to provide texture sampling for 3Doperations and media sampling for media operations. In some embodiments,sampler 2110 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2100 via thread spawningand dispatch logic. In some embodiments, thread execution logic 2100includes a local thread dispatcher 2104 that arbitrates threadinitiation requests from the graphics and media pipelines andinstantiates the requested threads on one or more execution units2108A-2108N. For example, the geometry pipeline (e.g., 2036 of FIG. 20)dispatches vertex processing, tessellation, or geometry processingthreads to thread execution logic 2100 (FIG. 21). In some embodiments,thread dispatcher 2104 can also process runtime thread spawning requestsfrom the executing shader programs.

Once a group of geometric objects has been processed and rasterized intopixel data, pixel shader 2102 is invoked to further compute outputinformation and cause results to be written to output surfaces (e.g.,color buffers, depth buffers, stencil buffers, etc.). In someembodiments, pixel shader 2102 calculates the values of the variousvertex attributes that are to be interpolated across the rasterizedobject. In some embodiments, pixel shader 2102 then executes anapplication programming interface (API)-supplied pixel shader program.To execute the pixel shader program, pixel shader 2102 dispatchesthreads to an execution unit (e.g., 2108A) via thread dispatcher 2104.In some embodiments, pixel shader 2102 uses texture sampling logic insampler 2110 to access texture data in texture maps stored in memory.Arithmetic operations on the texture data and the input geometry datacompute pixel color data for each geometric fragment, or discards one ormore pixels from further processing.

In some embodiments, the data port 2114 provides a memory accessmechanism for the thread execution logic 2100 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 2114 includes or couples to one or more cachememories (e.g., data cache 2112) to cache data for memory access via thedata port.

FIG. 22 is a block diagram illustrating a graphics processor instructionformats 2200 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2200 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit format 2210. A 64-bit compactedinstruction format 2230 is available for some instructions based on theselected instruction, instruction options, and number of operands. Thenative 128-bit format 2210 provides access to all instruction options,while some options and operations are restricted in the 64-bit format2230. The native instructions available in the 64-bit format 2230 varyby embodiment. In some embodiments, the instruction is compacted in partusing a set of index values in an index field 2213. The execution unithardware references a set of compaction tables based on the index valuesand uses the compaction table outputs to reconstruct a nativeinstruction in the 128-bit format 2210.

For each format, instruction opcode 2212 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2214 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). For 128-bitinstructions 2210 an exec-size field 2216 limits the number of datachannels that will be executed in parallel. In some embodiments,exec-size field 2216 is not available for use in the 64-bit compactinstruction format 2230.

Some execution unit instructions have up to three operands including twosource operands, src0 2220, src1 2222, and one destination 2218. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2224), where the instructionopcode 2212 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2210 includes anaccess/address mode information 2226 specifying, for example, whetherdirect register addressing mode or indirect register addressing mode isused. When direct register addressing mode is used, the register addressof one or more operands is directly provided by bits in the instruction2210.

In some embodiments, the 128-bit instruction format 2210 includes anaccess/address mode field 2226, which specifies an address mode and/oran access mode for the instruction. In one embodiment the access mode todefine a data access alignment for the instruction. Some embodimentssupport access modes including a 16-byte aligned access mode and a1-byte aligned access mode, where the byte alignment of the access modedetermines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction 2210 may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction 2210 may use 16-byte-aligned addressing for allsource and destination operands.

In one embodiment, the address mode portion of the access/address modefield 2226 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction 2210 directly provide the register address of one ormore operands. When indirect register addressing mode is used, theregister address of one or more operands may be computed based on anaddress register value and an address immediate field in theinstruction.

In some embodiments instructions are grouped based on opcode 2212bit-fields to simplify Opcode decode 2240. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2242 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2242 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2244 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2246 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2248 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2248 performs the arithmetic operations in parallelacross data channels. The vector math group 2250 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 23 is a block diagram of another embodiment of a graphics processor2300. Elements of FIG. 23 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 2300 includes a graphicspipeline 2320, a media pipeline 2330, a display engine 2340, threadexecution logic 2350, and a render output pipeline 2370. In someembodiments, graphics processor 2300 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2300 via a ring interconnect 2302. In someembodiments, ring interconnect 2302 couples graphics processor 2300 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 2302 areinterpreted by a command streamer 2303, which supplies instructions toindividual components of graphics pipeline 2320 or media pipeline 2330.

In some embodiments, command streamer 2303 directs the operation of avertex fetcher 2305 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2303. In someembodiments, vertex fetcher 2305 provides vertex data to a vertex shader2307, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 2305 andvertex shader 2307 execute vertex-processing instructions by dispatchingexecution threads to execution units 2352A, 2352B via a threaddispatcher 2331.

In some embodiments, execution units 2352A, 2352B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2352A, 2352B have anattached L1 cache 2351 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 2320 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 2311 configures thetessellation operations. A programmable domain shader 2317 providesback-end evaluation of tessellation output. A tessellator 2313 operatesat the direction of hull shader 2311 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 2320. Insome embodiments, if tessellation is not used, tessellation components2311, 2313, 2317 can be bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2319 via one or more threads dispatched to executionunits 2352A, 2352B, or can proceed directly to the clipper 2329. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader2319 receives input from the vertex shader 2307. In some embodiments,geometry shader 2319 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2329 processes vertex data. The clipper2329 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer 2373 (e.g., depth test component) in the render outputpipeline 2370 dispatches pixel shaders to convert the geometric objectsinto their per pixel representations. In some embodiments, pixel shaderlogic is included in thread execution logic 2350. In some embodiments,an application can bypass the rasterizer 2373 and access un-rasterizedvertex data via a stream out unit 2323.

The graphics processor 2300 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2352A, 2352B and associated cache(s) 2351,texture and media sampler 2354, and texture/sampler cache 2358interconnect via a data port 2356 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2354, caches 2351, 2358 and execution units2352A, 2352B each have separate memory access paths.

In some embodiments, render output pipeline 2370 contains a rasterizer2373 that converts vertex-based objects into an associated pixel-basedrepresentation. In some embodiments, the rasterizer logic includes awindower/masker unit to perform fixed function triangle and linerasterization. An associated render cache 2378 and depth cache 2379 arealso available in some embodiments. A pixel operations component 2377performs pixel-based operations on the data, though in some instances,pixel operations associated with 2D operations (e.g. bit block imagetransfers with blending) are performed by the 2D engine 2341, orsubstituted at display time by the display controller 2343 using overlaydisplay planes. In some embodiments, a shared L3 cache 2375 is availableto all graphics components, allowing the sharing of data without the useof main system memory.

In some embodiments, graphics processor media pipeline 2330 includes amedia engine 2337 and a video front end 2334. In some embodiments, videofront end 2334 receives pipeline commands from the command streamer2303. In some embodiments, media pipeline 2330 includes a separatecommand streamer. In some embodiments, video front-end 2334 processesmedia commands before sending the command to the media engine 2337. Insome embodiments, media engine 2337 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2350 via thread dispatcher 2331.

In some embodiments, graphics processor 2300 includes a display engine2340. In some embodiments, display engine 2340 is external to processor2300 and couples with the graphics processor via the ring interconnect2302, or some other interconnect bus or fabric. In some embodiments,display engine 2340 includes a 2D engine 2341 and a display controller2343. In some embodiments, display engine 2340 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2343 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 2320 and media pipeline 2330 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL) and Open Computing Language (OpenCL)from the Khronos Group, the Direct3D library from the MicrosoftCorporation, or support may be provided to both OpenGL and D3D. Supportmay also be provided for the Open Source Computer Vision Library(OpenCV). A future API with a compatible 3D pipeline would also besupported if a mapping can be made from the pipeline of the future APIto the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 24A is a block diagram illustrating a graphics processor commandformat 2400 according to some embodiments. FIG. 24B is a block diagramillustrating a graphics processor command sequence 2410 according to anembodiment. The solid lined boxes in FIG. 24A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 2400 of FIG. 24A includes data fields to identify atarget client 2402 of the command, a command operation code (opcode)2404, and the relevant data 2406 for the command. A sub-opcode 2405 anda command size 2408 are also included in some commands.

In some embodiments, client 2402 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 2404 and, if present, sub-opcode 2405 to determine theoperation to perform. The client unit performs the command usinginformation in data field 2406. For some commands an explicit commandsize 2408 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 24B shows an exemplary graphics processorcommand sequence 2410. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 2410 maybegin with a pipeline flush command 2412 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 2422 and the media pipeline 2424 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 2412 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 2413 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 2413is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command is 2412 isrequired immediately before a pipeline switch via the pipeline selectcommand 2413.

In some embodiments, a pipeline control command 2414 configures agraphics pipeline for operation and is used to program the 3D pipeline2422 and the media pipeline 2424. In some embodiments, pipeline controlcommand 2414 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 2414 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 2416 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 2416 includes selecting the size and number ofreturn buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 2420,the command sequence is tailored to the 3D pipeline 2422 beginning withthe 3D pipeline state 2430, or the media pipeline 2424 beginning at themedia pipeline state 2440.

The commands for the 3D pipeline state 2430 include 3D state settingcommands for vertex buffer state, vertex element state, constant colorstate, depth buffer state, and other state variables that are to beconfigured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based the particular 3DAPI in use. In some embodiments, 3D pipeline state 2430 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 2432 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 2432 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 2432command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 2432 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 2422 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 2422 is triggered via an execute 2434command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 2410follows the media pipeline 2424 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 2424 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIND vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 2424 is configured in a similarmanner as the 3D pipeline 2422. A set of media pipeline state commands2440 are dispatched or placed into in a command queue before the mediaobject commands 2442. In some embodiments, media pipeline state commands2440 include data to configure the media pipeline elements that will beused to process the media objects. This includes data to configure thevideo decode and video encode logic within the media pipeline, such asencode or decode format. In some embodiments, media pipeline statecommands 2440 also support the use one or more pointers to “indirect”state elements that contain a batch of state settings.

In some embodiments, media object commands 2442 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 2442. Once the pipeline state is configured andmedia object commands 2442 are queued, the media pipeline 2424 istriggered via an execute command 2444 or an equivalent execute event(e.g., register write). Output from media pipeline 2424 may then be postprocessed by operations provided by the 3D pipeline 2422 or the mediapipeline 2424. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 25 illustrates exemplary graphics software architecture for a dataprocessing system 2500 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application2510, an operating system 2520, and at least one processor 2530. In someembodiments, processor 2530 includes a graphics processor 2532 and oneor more general-purpose processor core(s) 2534. The graphics application2510 and operating system 2520 each execute in the system memory 2550 ofthe data processing system.

In some embodiments, 3D graphics application 2510 contains one or moreshader programs including shader instructions 2512. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 2514 in a machinelanguage suitable for execution by the general-purpose processor core2534. The application also includes graphics objects 2516 defined byvertex data.

In some embodiments, operating system 2520 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. When the Direct3D API is in use, theoperating system 2520 uses a front-end shader compiler 2524 to compileany shader instructions 2512 in HLSL into a lower-level shader language.The compilation may be a just-in-time (JIT) compilation or theapplication can perform shader pre-compilation. In some embodiments,high-level shaders are compiled into low-level shaders during thecompilation of the 3D graphics application 2510.

In some embodiments, user mode graphics driver 2526 contains a back-endshader compiler 2527 to convert the shader instructions 2512 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 2512 in the GLSL high-level language are passed to a usermode graphics driver 2526 for compilation. In some embodiments, usermode graphics driver 2526 uses operating system kernel mode functions2528 to communicate with a kernel mode graphics driver 2529. In someembodiments, kernel mode graphics driver 2529 communicates with graphicsprocessor 2532 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 26 is a block diagram illustrating an IP core development system2600 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2600 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2630 can generate a software simulation 2610 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation2610 can be used to design, test, and verify the behavior of the IPcore. A register transfer level (RTL) design can then be created orsynthesized from the simulation model 2600. The RTL design 2615 is anabstraction of the behavior of the integrated circuit that models theflow of digital signals between hardware registers, including theassociated logic performed using the modeled digital signals. Inaddition to an RTL design 2615, lower-level designs at the logic levelor transistor level may also be created, designed, or synthesized. Thus,the particular details of the initial design and simulation may vary.

The RTL design 2615 or equivalent may be further synthesized by thedesign facility into a hardware model 2620, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2665 using non-volatile memory 2640 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2650 or wireless connection 2660. Thefabrication facility 2665 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 27 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2700 that may be fabricated using one or more IPcores, according to an embodiment. The exemplary integrated circuitincludes one or more application processors 2705 (e.g., CPUs), at leastone graphics processor 2710, and may additionally include an imageprocessor 2715 and/or a video processor 2720, any of which may be amodular IP core from the same or multiple different design facilities.The integrated circuit includes peripheral or bus logic including a USBcontroller 2725, UART controller 2730, an SPI/SDIO controller 2735, andan I²S/I²C controller 2740. Additionally, the integrated circuit caninclude a display device 2745 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2750 and a mobileindustry processor interface (MIPI) display interface 2755. Storage maybe provided by a flash memory subsystem 2760 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2765 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2770.

Additionally, other logic and circuits may be included in the processorof integrated circuit 2700, including additional graphicsprocessors/cores, peripheral interface controllers, or general purposeprocessor cores.

Advantageously, any of the above systems, processors, graphicsprocessors, apparatuses, and/or methods may be integrated or configuredwith any of the various embodiments described herein (e.g. or portionsthereof), including, for example, those described in the followingAdditional Notes and Examples.

ADDITIONAL NOTES AND EXAMPLES

Example 1 may include an electronic processing system, comprising agraphics subsystem, persistent storage media communicatively coupled tothe graphics subsystem, memory communicatively coupled to the graphicssubsystem, a display communicatively coupled to the graphics subsystem,and a contextual configuration adjuster communicatively coupled to thegraphics subsystem to adjust a configuration of the graphics subsystembased on contextual information.

Example 2 may include the system of Example 1, wherein the contextualconfiguration adjuster comprises a context engine to determine thecontextual information.

Example 3 may include the system of Example 2, further comprising arecommendation engine communicatively coupled to the context engine todetermine a recommendation based on the contextual information.

Example 4 may include the system of Example 3, wherein the contextualconfiguration adjuster is further to adjust the configuration of thegraphics subsystem based at least in part on the recommendation from therecommendation engine.

Example 5 may include the system of any of Examples 1 to 4, furthercomprising a sense engine communicatively coupled to the contextualconfiguration adjuster to sense contextual data.

Example 6 may include the system of any of Examples 1 to 4, furthercomprising a profiler to determine profile information for a graphicsapplication, and a neural network trainer to train a neural network todevelop a configuration decision network for the graphics applicationbased on the profile information.

Example 7 may include the system of Example 6, wherein the contextualconfiguration adjuster is further to adjust the configuration of thegraphics subsystem based on the configuration decision network.

Example 8 may include a graphics apparatus, comprising a context engineto determine contextual information, a recommendation enginecommunicatively coupled to the context engine to determine arecommendation based on the contextual information, and a configurationengine communicatively coupled to the recommendation engine to adjust aconfiguration of a graphics operation based on the recommendation.

Example 9 may include the apparatus of Example 8, wherein the contextualinformation includes one or more of user, content, location, biometric,time, temperature, power, environmental, schedule, habit, andconfiguration settings for a prior frame.

Example 10 may include the apparatus of Example 8, wherein therecommendation includes one or more of a power setting, a performancesetting, a high performance mode, a balanced performance mode, a lowpower mode, a game mode, a movie mode, and a configuration setting for asubsequent frame.

Example 11 may include the apparatus of Example 8, wherein the graphicsoperation includes one or more of a resolution, a frame rate, a colorprecision, and a sampling rate.

Example 12 may include the apparatus of any of Examples 8 to 11, whereinthe context engine comprises a sense engine to sense contextual data,wherein the context engine is further to determine the contextualinformation based on the sensed contextual data.

Example 13 may include the apparatus of Example 12, wherein the sensedcontextual data includes one or more of a brightness of the environment,a temperature of the environment, machine vision information of theenvironment, movement information of the user, biometric information ofthe user, gesture information of the user, facial information of theuser, and machine vision information of the user.

Example 14 may include the apparatus of any of Examples 8 to 11, whereinthe recommendation engine comprises a recommendation store to store oneor more recommendations in association with saved contextualinformation, wherein the recommendation engine is further to compare thecontextual information from the context engine with the saved contextualinformation, and retrieve a stored recommendation associated with savedcontextual information based on the comparison.

Example 15 may include the apparatus of any of Examples 8 to 11, whereinthe recommendation engine further comprises a configuration decisionnetwork to input the contextual information, and output a recommendedconfiguration, wherein the recommendation engine is to determine therecommendation based on the recommended configuration from theconfiguration decision network.

Example 16 may include the apparatus of any of Examples 8 to 11, whereinthe recommendation is based on a predicted user preference for theconfiguration of a graphics operation.

Example 17 may include the apparatus of any of Examples 8 to 11, whereinthe context engine includes a profiler to determine profile informationfor a graphics application.

Example 18 may include the apparatus of Example 17, wherein therecommendation engine includes a neural network trainer to train aneural network to develop a configuration decision network for thegraphics application based on the profile information.

Example 19 may include the apparatus of Example 18, wherein theconfiguration engine is further to adjust the configuration of thegraphics operation based on the configuration decision network.

Example 20 may include a method of configuring graphics, comprisingdetermining contextual information, determining a recommendation basedon the contextual information, and adjusting a configuration of agraphics operation based on the recommendation.

Example 21 may include the method of Example 20, wherein the contextualinformation includes one or more of user, content, location, biometric,time, temperature, power, environmental, schedule, habit, andconfiguration settings for a prior frame.

Example 22 may include the method of Example 20, wherein therecommendation includes one or more of a power setting, a performancesetting, a high performance mode, a balanced performance mode, a lowpower mode, a game mode, a movie mode, and a configuration setting for asubsequent frame.

Example 23 may include the method of Example 20, wherein the graphicsoperation includes one or more of a resolution, a frame rate, a colorprecision, and a sampling rate.

Example 24 may include the method of any of Examples 20 to 23, furthercomprising sensing contextual data, and determining the contextualinformation based on the sensed contextual data.

Example 25 may include the method of Example 24, wherein the sensedcontextual data includes one or more of a brightness of the environment,a temperature of the environment, machine vision information of theenvironment, movement information of the user, biometric information ofthe user, gesture information of the user, facial information of theuser, and machine vision information of the user.

Example 26 may include the method of any of Examples 20 to 23, furthercomprising storing one or more recommendations in association with savedcontextual information, comparing the contextual information with thesaved contextual information, and retrieving a stored recommendationassociated with saved contextual information based on the comparison.

Example 27 may include the method of any of Examples 20 to 23, furthercomprising loading a configuration decision network, inputting thecontextual information into the configuration decision network,outputting a recommended configuration from the configuration decisionnetwork, and determining the recommendation based on the recommendedconfiguration from the configuration decision network.

Example 28 may include the method of any of Examples 20 to 23, furthercomprising predicting a user preference for the configuration of agraphics operation.

Example 29 may include the method of any of Examples 20 to 23, furthercomprising determining profile information for a graphics application.

Example 30 may include the method of Example 29, further comprisingtraining a neural network to develop a configuration decision networkfor the graphics application based on the profile information.

Example 31 may include the method of Example 30, further comprisingadjusting the configuration of the graphics operation based on theconfiguration decision network.

Example 32 may include at least one computer readable medium, comprisinga set of instructions, which when executed by a computing device causethe computing device to determine contextual information, determine arecommendation based on the contextual information, and adjust aconfiguration of a graphics operation based on the recommendation.

Example 33 may include the at least one computer readable medium ofExample 32, wherein the contextual information includes one or more ofuser, content, location, biometric, time, temperature, power,environmental, schedule, habit, and configuration settings for a priorframe.

Example 34 may include the at least one computer readable medium ofExample 32, wherein the recommendation includes one or more of a powersetting, a performance setting, a high performance mode, a balancedperformance mode, a low power mode, a game mode, a movie mode, and aconfiguration setting for a subsequent frame.

Example 35 may include the at least one computer readable medium ofExample 32, wherein the graphics operation includes one or more of aresolution, a frame rate, a color precision, and a sampling rate.

Example 36 may include the at least one computer readable medium of anyof Examples 32 to 35, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device to sensecontextual data, and determine the contextual information based on thesensed contextual data.

Example 37 may include the at least one computer readable medium ofExample 36, wherein the sensed contextual data includes one or more of abrightness of the environment, a temperature of the environment, machinevision information of the environment, movement information of the user,biometric information of the user, gesture information of the user,facial information of the user, and machine vision information of theuser.

Example 38 may include the at least one computer readable medium of anyof Examples 32 to 35, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device to storeone or more recommendations in association with saved contextualinformation, compare the contextual information with the savedcontextual information, and retrieve a stored recommendation associatedwith saved contextual information based on the comparison.

Example 39 may include the at least one computer readable medium of anyof Examples 32 to 35, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device to load aconfiguration decision network, input the contextual information intothe configuration decision network, output a recommended configurationfrom the configuration decision network, and determine therecommendation based on the recommended configuration from theconfiguration decision network.

Example 40 may include the at least one computer readable medium of anyof Examples 32 to 35, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device topredict a user preference for the configuration of a graphics operation.

Example 41 may include the at least one computer readable medium of anyof Examples 32 to 35, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device todetermine profile information for a graphics application.

Example 42 may include the at least one computer readable medium ofExample 41, comprising a further set of instructions, which whenexecuted by a computing device cause the computing device to train aneural network to develop a configuration decision network for thegraphics application based on the profile information.

Example 43 may include the at least one computer readable medium ofExample 42, comprising a further set of instructions, which whenexecuted by a computing device cause the computing device to adjust theconfiguration of the graphics operation based on the configurationdecision network.

Example 44 may include a graphics apparatus, comprising means fordetermining contextual information, means for determining arecommendation based on the contextual information, and means foradjusting a configuration of a graphics operation based on therecommendation.

Example 45 may include the apparatus of Example 44, wherein thecontextual information includes one or more of user, content, location,biometric, time, temperature, power, environmental, schedule, habit, andconfiguration settings for a prior frame.

Example 46 may include the apparatus of Example 44, wherein therecommendation includes one or more of a power setting, a performancesetting, a high performance mode, a balanced performance mode, a lowpower mode, a game mode, a movie mode, and a configuration setting for asubsequent frame.

Example 47 may include the apparatus of Example 44, wherein the graphicsoperation includes one or more of a resolution, a frame rate, a colorprecision, and a sampling rate.

Example 48 may include the apparatus of any of Examples 44 to 47,further comprising means for sensing contextual data, and means fordetermining the contextual information based on the sensed contextualdata.

Example 49 may include the apparatus of Example 48, wherein the sensedcontextual data includes one or more of a brightness of the environment,a temperature of the environment, machine vision information of theenvironment, movement information of the user, biometric information ofthe user, gesture information of the user, facial information of theuser, and machine vision information of the user.

Example 50 may include the apparatus of any of Examples 44 to 47,further comprising means for storing one or more recommendations inassociation with saved contextual information, means for comparing thecontextual information with the saved contextual information, and meansfor retrieving a stored recommendation associated with saved contextualinformation based on the comparison.

Example 51 may include the apparatus of any of Examples 44 to 47,further comprising means for loading a configuration decision network,means for inputting the contextual information into the configurationdecision network, means for outputting a recommended configuration fromthe configuration decision network, and means for determining therecommendation based on the recommended configuration from theconfiguration decision network.

Example 52 may include the apparatus of any of Examples 44 to 47,further comprising means for predicting a user preference for theconfiguration of a graphics operation.

Example 53 may include the apparatus of any of Examples 44 to 47,further comprising means for determining profile information for agraphics application.

Example 54 may include the apparatus of Example 53, further comprisingmeans for training a neural network to develop a configuration decisionnetwork for the graphics application based on the profile information.

Example 55 may include the apparatus of Example 54, further comprisingmeans for adjusting the configuration of the graphics operation based onthe configuration decision network.

Example 56 may include a graphics apparatus, comprising a profiler todetermine profile information for a graphics application, and aconfiguration developer communicatively coupled to the profiler todevelop a configuration decision network for the graphics applicationbased on the profile information.

Example 57 may include the apparatus of Example 56, wherein theconfiguration developer comprises a neural network trainer to train aneural network to develop the configuration decision network for thegraphics application based on the profile information.

Example 58 may include the apparatus of Example 57, wherein the profileinformation includes one or more of application execution time, waketime of a GPU unit, application states, active shaders, constant values,execution time of a unit of work (e.g. a draw or render pass), cachehit/miss rate, register pressure, memory usage, performance bottleneck,occupancy/busy-ness of a hardware block, and other hardware and softwaremetrics.

Example 59 may include the apparatus of Example 57, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Example 60 may include the graphics apparatus of any of Examples 56 to59, further comprising a graphics subsystem communicatively coupled tothe profiler to run the graphics application, and a configuration enginecommunicatively coupled to the graphics subsystem to adjust aconfiguration of a graphics operation based on the configurationdecision network.

Example 61 may include a method of configuring graphics, comprisingdetermining profile information for a graphics application, anddeveloping a configuration decision network for the graphics applicationbased on the profile information.

Example 62 may include the method of Example 61, further comprisingtraining a neural network to develop the configuration decision networkfor the graphics application based on the profile information.

Example 63 may include the method of Example 62, wherein the profileinformation includes one or more of application execution time, waketime of a GPU unit, application states, active shaders, constant values,execution time of a unit of work (e.g. a draw or render pass), cachehit/miss rate, register pressure, memory usage, performance bottleneck,occupancy/busy-ness of a hardware block, and other hardware and softwaremetrics.

Example 64 may include the method of Example 62, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Example 65 may include the method apparatus of any of Examples 61 to 64,further comprising running the graphics application, and adjusting aconfiguration of a graphics operation based on the configurationdecision network.

Example 66 may include at least one computer readable medium, comprisinga set of instructions, which when executed by a computing device causethe computing device to determine profile information for a graphicsapplication, and develop a configuration decision network for thegraphics application based on the profile information.

Example 67 may include the at least one computer readable medium ofExample 66, comprising a further set of instructions, which whenexecuted by a computing device cause the computing device to train aneural network to develop the configuration decision network for thegraphics application based on the profile information.

Example 68 may include the at least one computer readable medium ofExample 67, wherein the profile information includes one or more ofapplication execution time, wake time of a GPU unit, application states,active shaders, constant values, execution time of a unit of work (e.g.a draw or render pass), cache hit/miss rate, register pressure, memoryusage, performance bottleneck, occupancy/busy-ness of a hardware block,and other hardware and software metrics.

Example 69 may include the at least one computer readable medium ofExample 67, wherein the configuration decision network includes adecision path for one or more of: sleep time for a GPU unit, wake timefor a GPU unit, GPU frequency, ring frequency, CPU frequency, memoryfrequency, resolution, frame rate, sampling rate, cache configuration,dispatch width, resource cacheability settings, compiler optimizationsettings, thread group walk order for compute shaders, shadingfrequency, enabling or disabling hardware features, and powerconfiguration/state.

Example 70 may include the at least one computer readable medium of anyof Examples 66 to 69, comprising a further set of instructions, whichwhen executed by a computing device cause the computing device to runthe graphics application, and adjust a configuration of a graphicsoperation based on the configuration decision network.

Example 71 may include a graphics apparatus, comprising means fordetermining profile information for a graphics application, and meansfor developing a configuration decision network for the graphicsapplication based on the profile information.

Example 72 may include the apparatus of Example 71, further comprisingmeans for training a neural network to develop the configurationdecision network for the graphics application based on the profileinformation.

Example 73 may include the apparatus of Example 72, wherein the profileinformation includes one or more of application execution time, waketime of a GPU unit, application states, active shaders, constant values,execution time of a unit of work (e.g. a draw or render pass), cachehit/miss rate, register pressure, memory usage, performance bottleneck,occupancy/busy-ness of a hardware block, and other hardware and softwaremetrics.

Example 74 may include the apparatus of Example 72, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Example 75 may include the apparatus of any of Examples 71 to 74,further comprising means for running the graphics application, and meansfor adjusting a configuration of a graphics operation based on theconfiguration decision network.

Example 76 may include a graphics apparatus, comprising a graphicssubsystem to run a graphics application, a metrics monitorcommunicatively coupled to the graphics subsystem to determine metricsbased on resource utilization of the graphics application, and aconfiguration adjuster communicatively coupled to the graphics subsystemand the metrics monitor to apply a configuration decision network to themetrics to provide a configuration adjustment decision, and adjust aconfiguration of a graphics operation based on the configurationadjustment decision.

Example 77 may include the apparatus of Example 76, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Example 78 may include a method of configuring graphics, comprisingrunning a graphics application, determining metrics based on resourceutilization of the graphics application, applying a configurationdecision network to the metrics to provide a configuration adjustmentdecision, and adjusting a configuration of a graphics operation based onthe configuration adjustment decision.

Example 79 may include the method of Example 78, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Example 80 may include at least one computer readable medium, comprisinga set of instructions, which when executed by a computing device causethe computing device to run a graphics application, monitor resourceutilization of the graphics application to provide metrics, apply aconfiguration decision network to the metrics to provide a configurationadjustment decision, and adjust a configuration of a graphics operationbased on the configuration adjustment decision.

Example 81 may include the at least one computer readable medium ofExample 80, wherein the configuration decision network includes adecision path for one or more of: sleep time for a GPU unit, wake timefor a GPU unit, GPU frequency, ring frequency, CPU frequency, memoryfrequency, resolution, frame rate, sampling rate, cache configuration,dispatch width, resource cacheability settings, compiler optimizationsettings, thread group walk order for compute shaders, shadingfrequency, enabling or disabling hardware features, and powerconfiguration/state.

Example 82 may include a graphics apparatus, comprising means forrunning a graphics application, means for monitoring resourceutilization of the graphics application to provide metrics, means forapplying a configuration decision network to the metrics to provide aconfiguration adjustment decision, and means for adjusting aconfiguration of a graphics operation based on the configurationadjustment decision.

Example 83 may include the apparatus of Example 82, wherein theconfiguration decision network includes a decision path for one or moreof: sleep time for a GPU unit, wake time for a GPU unit, GPU frequency,ring frequency, CPU frequency, memory frequency, resolution, frame rate,sampling rate, cache configuration, dispatch width, resourcecacheability settings, compiler optimization settings, thread group walkorder for compute shaders, shading frequency, enabling or disablinghardware features, and power configuration/state.

Embodiments are applicable for use with all types of semiconductorintegrated circuit (“IC”) chips. Examples of these IC chips include butare not limited to processors, controllers, chipset components,programmable logic arrays (PLAs), memory chips, network chips, systemson chip (SoCs), SSD/NAND controller ASICs, and the like. In addition, insome of the drawings, signal conductor lines are represented with lines.Some may be different, to indicate more constituent signal paths, have anumber label, to indicate a number of constituent signal paths, and/orhave arrows at one or more ends, to indicate primary information flowdirection. This, however, should not be construed in a limiting manner.Rather, such added detail may be used in connection with one or moreexemplary embodiments to facilitate easier understanding of a circuit.Any represented signal lines, whether or not having additionalinformation, may actually comprise one or more signals that may travelin multiple directions and may be implemented with any suitable type ofsignal scheme, e.g., digital or analog lines implemented withdifferential pairs, optical fiber lines, and/or single-ended lines.

Example sizes/models/values/ranges may have been given, althoughembodiments are not limited to the same. As manufacturing techniques(e.g., photolithography) mature over time, it is expected that devicesof smaller size could be manufactured. In addition, well knownpower/ground connections to IC chips and other components may or may notbe shown within the figures, for simplicity of illustration anddiscussion, and so as not to obscure certain aspects of the embodiments.Further, arrangements may be shown in block diagram form in order toavoid obscuring embodiments, and also in view of the fact that specificswith respect to implementation of such block diagram arrangements arehighly dependent upon the platform within which the embodiment is to beimplemented, i.e., such specifics should be well within purview of oneskilled in the art. Where specific details (e.g., circuits) are setforth in order to describe example embodiments, it should be apparent toone skilled in the art that embodiments can be practiced without, orwith variation of, these specific details. The description is thus to beregarded as illustrative instead of limiting.

The term “coupled” may be used herein to refer to any type ofrelationship, direct or indirect, between the components in question,and may apply to electrical, mechanical, fluid, optical,electromagnetic, electromechanical or other connections. In addition,the terms “first”, “second”, etc. may be used herein only to facilitatediscussion, and carry no particular temporal or chronologicalsignificance unless otherwise indicated. Additionally, it is understoodthat the indefinite articles “a” or “an” carries the meaning of “one ormore” or “at least one”.

As used in this application and in the claims, a list of items joined bythe term “one or more of” may mean any combination of the listed terms.For example, the phrases “one or more of A, B or C” may mean A, B, C; Aand B; A and C; B and C; or A, B and C.

The embodiments have been described above with reference to specificembodiments. Persons skilled in the art, however, will understand thatvarious modifications and changes may be made thereto without departingfrom the broader spirit and scope of the embodiments as set forth in theappended claims. The foregoing description and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. (canceled)
 2. An electronic processing system, comprising: a graphicssubsystem to run a graphics application; a profiler to determine profileinformation for the graphics application; a configuration developercommunicatively coupled to the profiler to develop a configurationdecision network for the graphics application based on the profileinformation; and a configuration engine communicatively coupled to thegraphics subsystem to adjust a configuration of a graphics operation ofthe graphics subsystem based on the configuration decision network. 3.The system of claim 2, wherein the configuration developer includes aneural network trainer to train a neural network for the configurationdecision network.
 4. The system of claim 3, wherein after the neuralnetwork is trained, the neural network provides a decision tree to beused during runtime to determine a graphics configuration.
 5. The systemof claim 4, wherein the decision tree includes a decision path for oneor more of a sleep time for the graphics processing unit (GPU), a waketime for the GPU, a GPU frequency, a ring frequency, a centralprocessing unit (CPU) frequency, a memory frequency, a resolution, aframe rate, a sampling rate, a cache configuration, a dispatch width,resource cacheability settings, compiler optimization settings, a threadgroup walk order for compute shaders, a shading frequency, an enablingof a hardware feature, a disabling of a hardware feature, or a powerconfiguration.
 6. The system of claim 5, wherein the profile informationincludes one or more of an application execution time, a wake time of aGPU, application states, active shaders, constant values, an executiontime of a unit of work, a cache hit and miss rate, a register pressure,a memory usage, a performance bottleneck, or an occupancy of a hardwareblock.
 7. The system of claim 2, wherein the graphics subsystem includesa render engine and the configuration engine is to adjust one or moreparameters of the render engine.
 8. A semiconductor apparatus comprisinglogic implemented at least partly in one or more of configurable logicor fixed-functionality hardware logic, the logic to: determine profileinformation for a graphics application; develop a configuration decisionnetwork for the graphics application based on the profile information;run a graphics application; and adjust a configuration of a graphicsoperation based on the configuration decision network.
 9. The apparatusof claim 8, wherein to develop a configuration decision networkcomprises to train a neural network for the configuration decisionnetwork.
 10. The apparatus of claim 9, wherein the neural network, oncetrained, provides a decision tree to be used during runtime to determinea graphics configuration.
 11. The apparatus of claim 10, wherein thedecision tree includes a decision path for one or more of a sleep timefor the graphics processing unit (GPU), a wake time for the GPU, a GPUfrequency, a ring frequency, a central processing unit (CPU) frequency,a memory frequency, a resolution, a frame rate, a sampling rate, a cacheconfiguration, a dispatch width, resource cacheability settings,compiler optimization settings, a thread group walk order for computeshaders, a shading frequency, an enabling of a hardware feature, adisabling of a hardware feature, or a power configuration.
 12. Theapparatus of claim 11, wherein the profile information includes one ormore of an application execution time, a wake time of a GPU, applicationstates, active shaders, constant values, an execution time of a unit ofwork, a cache hit and miss rate, a register pressure, a memory usage, aperformance bottleneck, or an occupancy of a hardware block.
 13. Theapparatus of claim 8, wherein to adjust a configuration of a graphicsoperation includes to adjust one or more parameters of a graphics renderengine.
 14. At least one non-transitory computer readable storage mediumcomprising a set of instructions which, when executed by a computingdevice, cause the computing device to: determine profile information fora graphics application; develop a configuration decision network for thegraphics application based on the profile information; run a graphicsapplication; and adjust a configuration of a graphics operation based onthe configuration decision network.
 15. The at least one non-transitorycomputer readable storage medium of claim 14, wherein to develop aconfiguration decision network comprises to train a neural network forthe configuration decision network.
 16. The at least one non-transitorycomputer readable storage medium of claim 15, wherein the neuralnetwork, once trained, provides a decision tree to be used duringruntime to determine a graphics configuration.
 17. The at least onenon-transitory computer readable storage medium of claim 16, wherein thedecision tree includes a decision path for one or more of a sleep timefor the graphics processing unit (GPU), a wake time for the GPU, a GPUfrequency, a ring frequency, a central processing unit (CPU) frequency,a memory frequency, a resolution, a frame rate, a sampling rate, a cacheconfiguration, a dispatch width, resource cacheability settings,compiler optimization settings, a thread group walk order for computeshaders, a shading frequency, an enabling of a hardware feature, adisabling of a hardware feature, or a power configuration.
 18. The atleast one non-transitory computer readable storage medium of claim 17,wherein the profile information includes one or more of an applicationexecution time, a wake time of a GPU, application states, activeshaders, constant values, an execution time of a unit of work, a cachehit and miss rate, a register pressure, a memory usage, a performancebottleneck, or an occupancy of a hardware block.
 19. The at least onenon-transitory computer readable storage medium of claim 14, whereinwherein to adjust a configuration of a graphics operation includes toadjust one or more parameters of a graphics render engine.
 20. A methodcomprising: determining profile information for a graphics application;developing a configuration decision network for the graphics applicationbased on the profile information; running a graphics application; andadjusting a configuration of a graphics operation based on theconfiguration decision network.
 21. The method of claim 20, whereindeveloping a configuration decision network comprises training a neuralnetwork for the configuration decision network.
 22. The method of claim21, wherein the neural network, once trained, provides a decision treeto be used during runtime for determining a graphics configuration. 23.The method of claim 22, wherein the decision tree includes a decisionpath for one or more of a sleep time for the graphics processing unit(GPU), a wake time for the GPU, a GPU frequency, a ring frequency, acentral processing unit (CPU) frequency, a memory frequency, aresolution, a frame rate, a sampling rate, a cache configuration, adispatch width, resource cacheability settings, compiler optimizationsettings, a thread group walk order for compute shaders, a shadingfrequency, an enabling of a hardware feature, a disabling of a hardwarefeature, or a power configuration.
 24. The method of claim 23, whereinthe profile information includes one or more of an application executiontime, a wake time of a GPU, application states, active shaders, constantvalues, an execution time of a unit of work, a cache hit and miss rate,a register pressure, a memory usage, a performance bottleneck, or anoccupancy of a hardware block.
 25. The method of claim 20, whereinadjusting a configuration of a graphics operation includes adjusting oneor more parameters of a graphics render engine.